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code/pointcept/datasets/__init__.py

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+from .defaults import DefaultDataset, ConcatDataset
+from .builder import build_dataset
+from .utils import point_collate_fn, collate_fn
+
+# indoor scene
+from .s3dis import S3DISDataset
+from .scannet import ScanNetDataset, ScanNet200Dataset
+from .scannet_pair import ScanNetPairDataset
+from .arkitscenes import ArkitScenesDataset
+from .structure3d import Structured3DDataset
+
+# outdoor scene
+from .semantic_kitti import SemanticKITTIDataset
+from .nuscenes import NuScenesDataset
+from .waymo import WaymoDataset
+from .itri import ItriDataset
+
+# object
+from .modelnet import ModelNetDataset
+from .shapenet_part import ShapeNetPartDataset
+
+# dataloader
+from .dataloader import MultiDatasetDataloader

code/pointcept/datasets/arkitscenes.py

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+"""
+ArkitScenes Dataset
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose, TRANSFORMS
+from .preprocessing.scannet.meta_data.scannet200_constants import VALID_CLASS_IDS_200
+
+
+@DATASETS.register_module()
+class ArkitScenesDataset(Dataset):
+    def __init__(
+        self,
+        split="Training",
+        data_root="data/ARKitScenesMesh",
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+    ):
+        super(ArkitScenesDataset, self).__init__()
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.loop = (
+            loop if not test_mode else 1
+        )  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+        self.class2id = np.array(VALID_CLASS_IDS_200)
+
+        if test_mode:
+            self.test_voxelize = TRANSFORMS.build(self.test_cfg.voxelize)
+            self.test_crop = TRANSFORMS.build(self.test_cfg.crop)
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in {} set.".format(
+                len(self.data_list), self.loop, split
+            )
+        )
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+        elif isinstance(self.split, list):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data(self, idx):
+        data = torch.load(self.data_list[idx % len(self.data_list)])
+        coord = data["coord"]
+        color = data["color"]
+        normal = data["normal"]
+        segment = np.zeros(coord.shape[0])
+        data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+        return data_dict
+
+    def get_data_name(self, idx):
+        data_idx = self.data_idx[idx % len(self.data_idx)]
+        return os.path.basename(self.data_list[data_idx]).split(".")[0]
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        segment = data_dict.pop("segment")
+        data_dict = self.transform(data_dict)
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+
+        input_dict_list = []
+        for data in data_dict_list:
+            data_part_list = self.test_voxelize(data)
+            for data_part in data_part_list:
+                data_part_list = self.test_crop(data_part)
+                input_dict_list += data_part_list
+
+        for i in range(len(input_dict_list)):
+            input_dict_list[i] = self.post_transform(input_dict_list[i])
+        return input_dict_list, segment
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop

code/pointcept/datasets/builder.py

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+"""
+Dataset Builder
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+from pointcept.utils.registry import Registry
+
+DATASETS = Registry("datasets")
+
+
+def build_dataset(cfg):
+    """Build datasets."""
+    return DATASETS.build(cfg)

code/pointcept/datasets/dataloader.py

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+from functools import partial
+import weakref
+import torch
+import torch.utils.data
+
+import pointcept.utils.comm as comm
+from pointcept.datasets.utils import point_collate_fn
+from pointcept.datasets import ConcatDataset
+from pointcept.utils.env import set_seed
+
+
+class MultiDatasetDummySampler:
+    def __init__(self):
+        self.dataloader = None
+
+    def set_epoch(self, epoch):
+        if comm.get_world_size() > 1:
+            for dataloader in self.dataloader.dataloaders:
+                dataloader.sampler.set_epoch(epoch)
+        return
+
+
+class MultiDatasetDataloader:
+    """
+    Multiple Datasets Dataloader, batch data from a same dataset and mix up ratio determined by loop of each sub dataset.
+    The overall length is determined by the main dataset (first) and loop of concat dataset.
+    """
+
+    def __init__(
+        self,
+        concat_dataset: ConcatDataset,
+        batch_size_per_gpu: int,
+        num_worker_per_gpu: int,
+        mix_prob=0,
+        seed=None,
+    ):
+        self.datasets = concat_dataset.datasets
+        self.ratios = [dataset.loop for dataset in self.datasets]
+        # reset data loop, original loop serve as ratios
+        for dataset in self.datasets:
+            dataset.loop = 1
+        # determine union training epoch by main dataset
+        self.datasets[0].loop = concat_dataset.loop
+        # build sub-dataloaders
+        num_workers = num_worker_per_gpu // len(self.datasets)
+        self.dataloaders = []
+        for dataset_id, dataset in enumerate(self.datasets):
+            if comm.get_world_size() > 1:
+                sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+            else:
+                sampler = None
+
+            init_fn = (
+                partial(
+                    self._worker_init_fn,
+                    dataset_id=dataset_id,
+                    num_workers=num_workers,
+                    num_datasets=len(self.datasets),
+                    rank=comm.get_rank(),
+                    seed=seed,
+                )
+                if seed is not None
+                else None
+            )
+            self.dataloaders.append(
+                torch.utils.data.DataLoader(
+                    dataset,
+                    batch_size=batch_size_per_gpu,
+                    shuffle=(sampler is None),
+                    num_workers=num_worker_per_gpu,
+                    sampler=sampler,
+                    collate_fn=partial(point_collate_fn, mix_prob=mix_prob),
+                    pin_memory=True,
+                    worker_init_fn=init_fn,
+                    drop_last=True,
+                    persistent_workers=True,
+                )
+            )
+        self.sampler = MultiDatasetDummySampler()
+        self.sampler.dataloader = weakref.proxy(self)
+
+    def __iter__(self):
+        iterator = [iter(dataloader) for dataloader in self.dataloaders]
+        while True:
+            for i in range(len(self.ratios)):
+                for _ in range(self.ratios[i]):
+                    try:
+                        batch = next(iterator[i])
+                    except StopIteration:
+                        if i == 0:
+                            return
+                        else:
+                            iterator[i] = iter(self.dataloaders[i])
+                            batch = next(iterator[i])
+                    yield batch
+
+    def __len__(self):
+        main_data_loader_length = len(self.dataloaders[0])
+        return (
+            main_data_loader_length // self.ratios[0] * sum(self.ratios)
+            + main_data_loader_length % self.ratios[0]
+        )
+
+    @staticmethod
+    def _worker_init_fn(worker_id, num_workers, dataset_id, num_datasets, rank, seed):
+        worker_seed = (
+            num_workers * num_datasets * rank
+            + num_workers * dataset_id
+            + worker_id
+            + seed
+        )
+        set_seed(worker_seed)

code/pointcept/datasets/defaults.py

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+"""
+Default Datasets
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import glob
+import numpy as np
+import torch
+from copy import deepcopy
+from torch.utils.data import Dataset
+from collections.abc import Sequence
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS, build_dataset
+from .transform import Compose, TRANSFORMS
+import traceback
+
+
+
+@DATASETS.register_module()
+class DefaultDataset(Dataset):
+    def __init__(
+        self,
+        split="train",
+        data_root="data/dataset",
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+    ):
+        super(DefaultDataset, self).__init__()
+        self.data_root = data_root
+        self.split = split
+        self.transform = Compose(transform)
+        self.loop = (
+            loop if not test_mode else 1
+        )  # force make loop = 1 while in test mode
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+
+        if test_mode:
+            self.test_voxelize = (
+                TRANSFORMS.build(self.test_cfg.voxelize)
+                if self.test_cfg.voxelize is not None
+                else None
+            )
+            self.test_crop = (
+                TRANSFORMS.build(self.test_cfg.crop)
+                if self.test_cfg.crop is not None
+                else None
+            )
+            self.post_transform = Compose(self.test_cfg.post_transform)
+            self.aug_transform = [Compose(aug) for aug in self.test_cfg.aug_transform]
+
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in {} set.".format(
+                len(self.data_list), self.loop, split
+            )
+        )
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth"))
+        elif isinstance(self.split, Sequence):
+            data_list = []
+            for split in self.split:
+                data_list += glob.glob(os.path.join(self.data_root, split, "*.pth"))
+        else:
+            raise NotImplementedError
+        return data_list
+
+    def get_data(self, idx):
+        data = torch.load(self.data_list[idx % len(self.data_list)])
+        coord = data["coord"]
+        color = data["color"]
+        normal = data["normal"]
+        if "semantic_gt" in data.keys():
+            segment = data["semantic_gt"].reshape([-1])
+        else:
+            segment = np.ones(coord.shape[0]) * -1
+        data_dict = dict(coord=coord, normal=normal, color=color, segment=segment)
+        return data_dict
+
+    def get_data_name(self, idx):
+        return os.path.basename(self.data_list[idx % len(self.data_list)]).split(".")[0]
+
+    def prepare_train_data(self, idx):
+        # load data
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        # print("prepare_test_data called with idx:", idx)
+        # print("Traceback:")
+        # for line in traceback.format_stack():
+            # print(line.strip())
+        # load data
+        print("prepare_test_data called with idx:", idx)
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        result_dict = dict(
+            segment=data_dict.pop("segment"), name=self.get_data_name(idx)
+        )
+        if "origin_segment" in data_dict:
+            assert "inverse" in data_dict
+            result_dict["origin_segment"] = data_dict.pop("origin_segment")
+            result_dict["inverse"] = data_dict.pop("inverse")
+
+        data_dict_list = []
+        for aug in self.aug_transform:
+            data_dict_list.append(aug(deepcopy(data_dict)))
+
+        fragment_list = []
+        for data in data_dict_list:
+            if self.test_voxelize is not None:
+                data_part_list = self.test_voxelize(data)
+            else:
+                data["index"] = np.arange(data["coord"].shape[0])
+                data_part_list = [data]
+            for data_part in data_part_list:
+                if self.test_crop is not None:
+                    data_part = self.test_crop(data_part)
+                else:
+                    data_part = [data_part]
+                fragment_list += data_part
+
+        for i in range(len(fragment_list)):
+            fragment_list[i] = self.post_transform(fragment_list[i])
+        result_dict["fragment_list"] = fragment_list
+        return result_dict
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop
+
+
+@DATASETS.register_module()
+class ConcatDataset(Dataset):
+    def __init__(self, datasets, loop=1):
+        super(ConcatDataset, self).__init__()
+        self.datasets = [build_dataset(dataset) for dataset in datasets]
+        self.loop = loop
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in the concat set.".format(
+                len(self.data_list), self.loop
+            )
+        )
+
+    def get_data_list(self):
+        data_list = []
+        for i in range(len(self.datasets)):
+            data_list.extend(
+                zip(
+                    np.ones(len(self.datasets[i])) * i, np.arange(len(self.datasets[i]))
+                )
+            )
+        return data_list
+
+    def get_data(self, idx):
+        dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+        return self.datasets[dataset_idx][data_idx]
+
+    def get_data_name(self, idx):
+        dataset_idx, data_idx = self.data_list[idx % len(self.data_list)]
+        return self.datasets[dataset_idx].get_data_name(data_idx)
+
+    def __getitem__(self, idx):
+        return self.get_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop

code/pointcept/datasets/itri.py

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+"""
+Semantic KITTI dataset
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+import glob
+
+@DATASETS.register_module()
+class ItriDataset(DefaultDataset):
+    def __init__(
+        self,
+        split="train",
+        data_root="data/itri",
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+        ignore_index=-1,
+    ):
+        self.ignore_index = ignore_index
+        self.learning_map = self.get_learning_map(ignore_index)
+        self.learning_map_inv = self.get_learning_map_inv(ignore_index)
+        super().__init__(
+            split=split,
+            data_root=data_root,
+            transform=transform,
+            test_mode=test_mode,
+            test_cfg=test_cfg,
+            loop=loop,
+        )
+
+    def get_data_list(self):
+        split2seq = dict(
+            train=[0, 1, 2, 3, 4, 5, 6, 7, 9, 10],
+            val=[0,2,5,6,7,8,9,10],
+            test=[11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21],
+        )
+        if isinstance(self.split, str):
+            seq_list = split2seq[self.split]
+        elif isinstance(self.split, list):
+            seq_list = []
+            for split in self.split:
+                seq_list += split2seq[split]
+        else:
+            raise NotImplementedError
+
+        data_list = []
+        for seq in seq_list:
+            seq = str(seq).zfill(2)
+            seq_folder = os.path.join(self.data_root, "dataset", "sequences", seq)
+            seq_files = sorted(os.listdir(os.path.join(seq_folder, "velodyne")))
+            data_list += [
+                os.path.join(seq_folder, "velodyne", file) for file in seq_files
+            ]
+        return data_list
+
+    def get_data(self, idx):
+        data_path = self.data_list[idx % len(self.data_list)]
+        with open(data_path, "rb") as b:
+            scan = np.fromfile(b, dtype=np.float32).reshape(-1, 4)
+        coord = scan[:, :3]
+        strength = scan[:, -1].reshape([-1, 1])
+
+        label_file = data_path.replace("velodyne", "labels").replace(".bin", ".label")
+        if os.path.exists(label_file):
+            with open(label_file, "rb") as a:
+                segment = np.fromfile(a, dtype=np.int32).reshape(-1)
+                segment = np.vectorize(self.learning_map.__getitem__)(
+                    segment & 0xFFFF
+                ).astype(np.int32)
+        else:
+            segment = np.zeros(scan.shape[0]).astype(np.int32)
+        #data_dict = dict(coord=coord, strength=strength, segment=segment)
+        data_dict = dict(
+            coord=coord,
+            strength=strength,
+            segment=segment,
+            name=self.get_data_name(idx),
+        )
+        return data_dict
+
+    def get_data_name(self, idx):
+        file_path = self.data_list[idx % len(self.data_list)]
+        dir_path, file_name = os.path.split(file_path)
+        sequence_name = os.path.basename(os.path.dirname(dir_path))
+        frame_name = os.path.splitext(file_name)[0]
+        data_name = f"{sequence_name}_{frame_name}"
+        return data_name
+
+    @staticmethod
+    def get_learning_map(ignore_index):
+        learning_map = {
+            0: ignore_index,  # "unlabeled"
+            1: ignore_index,  # "outlier" mapped to "unlabeled" --------------------------mapped
+            10: 0,  # "car"
+            11: 1,  # "bicycle"
+            13: 4,  # "bus" mapped to "other-vehicle" --------------------------mapped
+            15: 2,  # "motorcycle"
+            16: 4,  # "on-rails" mapped to "other-vehicle" ---------------------mapped
+            18: 3,  # "truck"
+            20: 4,  # "other-vehicle"
+            30: 5,  # "person"
+            31: 6,  # "bicyclist"
+            32: 7,  # "motorcyclist"
+            40: 8,  # "road"
+            44: 9,  # "parking"
+            48: 10,  # "sidewalk"
+            49: 11,  # "other-ground"
+            50: 12,  # "building"
+            51: 13,  # "fence"
+            52: ignore_index,  # "other-structure" mapped to "unlabeled" ------------------mapped
+            60: 8,  # "lane-marking" to "road" ---------------------------------mapped
+            70: 14,  # "vegetation"
+            71: 15,  # "trunk"
+            72: 16,  # "terrain"
+            80: 17,  # "pole"
+            81: 18,  # "traffic-sign"
+            99: ignore_index,  # "other-object" to "unlabeled" ----------------------------mapped
+            252: 0,  # "moving-car" to "car" ------------------------------------mapped
+            253: 6,  # "moving-bicyclist" to "bicyclist" ------------------------mapped
+            254: 5,  # "moving-person" to "person" ------------------------------mapped
+            255: 7,  # "moving-motorcyclist" to "motorcyclist" ------------------mapped
+            256: 4,  # "moving-on-rails" mapped to "other-vehicle" --------------mapped
+            257: 4,  # "moving-bus" mapped to "other-vehicle" -------------------mapped
+            258: 3,  # "moving-truck" to "truck" --------------------------------mapped
+            259: 4,  # "moving-other"-vehicle to "other-vehicle" ----------------mapped
+        }
+        return learning_map
+
+    @staticmethod
+    def get_learning_map_inv(ignore_index):
+        learning_map_inv = {
+            ignore_index: ignore_index,  # "unlabeled"
+            0: 10,  # "car"
+            1: 11,  # "bicycle"
+            2: 15,  # "motorcycle"
+            3: 18,  # "truck"
+            4: 20,  # "other-vehicle"
+            5: 30,  # "person"
+            6: 31,  # "bicyclist"
+            7: 32,  # "motorcyclist"
+            8: 40,  # "road"
+            9: 44,  # "parking"
+            10: 48,  # "sidewalk"
+            11: 49,  # "other-ground"
+            12: 50,  # "building"
+            13: 51,  # "fence"
+            14: 70,  # "vegetation"
+            15: 71,  # "trunk"
+            16: 72,  # "terrain"
+            17: 80,  # "pole"
+            18: 81,  # "traffic-sign"
+        }
+        return learning_map_inv

code/pointcept/datasets/modelnet.py

@@ -0,0 +1,104 @@
+"""
+ModelNet40 Dataset
+
+get sampled point clouds of ModelNet40 (XYZ and normal from mesh, 10k points per shape)
+at "https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip"
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+from torch.utils.data import Dataset
+
+from pointcept.utils.logger import get_root_logger
+from .builder import DATASETS
+from .transform import Compose
+
+
+@DATASETS.register_module()
+class ModelNetDataset(Dataset):
+    def __init__(
+        self,
+        split="train",
+        data_root="data/modelnet40_normal_resampled",
+        class_names=None,
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        cache_data=False,
+        loop=1,
+    ):
+        super(ModelNetDataset, self).__init__()
+        self.data_root = data_root
+        self.class_names = dict(zip(class_names, range(len(class_names))))
+        self.split = split
+        self.transform = Compose(transform)
+        self.loop = (
+            loop if not test_mode else 1
+        )  # force make loop = 1 while in test mode
+        self.cache_data = cache_data
+        self.test_mode = test_mode
+        self.test_cfg = test_cfg if test_mode else None
+        self.cache = {}
+        if test_mode:
+            # TODO: Optimize
+            pass
+
+        self.data_list = self.get_data_list()
+        logger = get_root_logger()
+        logger.info(
+            "Totally {} x {} samples in {} set.".format(
+                len(self.data_list), self.loop, split
+            )
+        )
+
+    def get_data_list(self):
+        assert isinstance(self.split, str)
+        split_path = os.path.join(
+            self.data_root, "modelnet40_{}.txt".format(self.split)
+        )
+        data_list = np.loadtxt(split_path, dtype="str")
+        return data_list
+
+    def get_data(self, idx):
+        data_idx = idx % len(self.data_list)
+        if self.cache_data:
+            coord, normal, category = self.cache[data_idx]
+        else:
+            data_shape = "_".join(self.data_list[data_idx].split("_")[0:-1])
+            data_path = os.path.join(
+                self.data_root, data_shape, self.data_list[data_idx] + ".txt"
+            )
+            data = np.loadtxt(data_path, delimiter=",").astype(np.float32)
+            coord, normal = data[:, 0:3], data[:, 3:6]
+            category = np.array([self.class_names[data_shape]])
+            if self.cache_data:
+                self.cache[data_idx] = (coord, normal, category)
+        data_dict = dict(coord=coord, normal=normal, category=category)
+        return data_dict
+
+    def prepare_train_data(self, idx):
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def prepare_test_data(self, idx):
+        assert idx < len(self.data_list)
+        data_dict = self.get_data(idx)
+        data_dict = self.transform(data_dict)
+        return data_dict
+
+    def get_data_name(self, idx):
+        data_idx = idx % len(self.data_list)
+        return self.data_list[data_idx]
+
+    def __getitem__(self, idx):
+        if self.test_mode:
+            return self.prepare_test_data(idx)
+        else:
+            return self.prepare_train_data(idx)
+
+    def __len__(self):
+        return len(self.data_list) * self.loop

code/pointcept/datasets/nuscenes.py

@@ -0,0 +1,137 @@
+"""
+nuScenes Dataset
+
+Author: Xiaoyang Wu ([email protected]), Zheng Zhang
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import numpy as np
+from collections.abc import Sequence
+import pickle
+
+from .builder import DATASETS
+from .defaults import DefaultDataset
+
+
+@DATASETS.register_module()
+class NuScenesDataset(DefaultDataset):
+    def __init__(
+        self,
+        split="train",
+        data_root="data/nuscenes",
+        sweeps=10,
+        transform=None,
+        test_mode=False,
+        test_cfg=None,
+        loop=1,
+        ignore_index=-1,
+    ):
+        self.sweeps = sweeps
+        self.ignore_index = ignore_index
+        self.learning_map = self.get_learning_map(ignore_index)
+        super().__init__(
+            split=split,
+            data_root=data_root,
+            transform=transform,
+            test_mode=test_mode,
+            test_cfg=test_cfg,
+            loop=loop,
+        )
+
+    def get_info_path(self, split):
+        assert split in ["train", "val", "test"]
+        if split == "train":
+            return os.path.join(
+                self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_train.pkl"
+            )
+        elif split == "val":
+            return os.path.join(
+                self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_val.pkl"
+            )
+        elif split == "test":
+            return os.path.join(
+                self.data_root, "info", f"nuscenes_infos_{self.sweeps}sweeps_test.pkl"
+            )
+        else:
+            raise NotImplementedError
+
+    def get_data_list(self):
+        if isinstance(self.split, str):
+            info_paths = [self.get_info_path(self.split)]
+        elif isinstance(self.split, Sequence):
+            info_paths = [self.get_info_path(s) for s in self.split]
+        else:
+            raise NotImplementedError
+        data_list = []
+        for info_path in info_paths:
+            with open(info_path, "rb") as f:
+                info = pickle.load(f)
+                data_list.extend(info)
+        return data_list
+
+    def get_data(self, idx):
+        data = self.data_list[idx % len(self.data_list)]
+        lidar_path = os.path.join(self.data_root, "raw", data["lidar_path"])
+        points = np.fromfile(str(lidar_path), dtype=np.float32, count=-1).reshape(
+            [-1, 5]
+        )
+        coord = points[:, :3]
+        strength = points[:, 3].reshape([-1, 1]) / 255  # scale strength to [0, 1]
+
+        if "gt_segment_path" in data.keys():
+            gt_segment_path = os.path.join(
+                self.data_root, "raw", data["gt_segment_path"]
+            )
+            segment = np.fromfile(
+                str(gt_segment_path), dtype=np.uint8, count=-1
+            ).reshape([-1])
+            segment = np.vectorize(self.learning_map.__getitem__)(segment).astype(
+                np.int64
+            )
+        else:
+            segment = np.ones((points.shape[0],), dtype=np.int64) * self.ignore_index
+        data_dict = dict(coord=coord, strength=strength, segment=segment)
+        return data_dict
+
+    def get_data_name(self, idx):
+        # return data name for lidar seg, optimize the code when need to support detection
+        return self.data_list[idx % len(self.data_list)]["lidar_token"]
+
+    @staticmethod
+    def get_learning_map(ignore_index):
+        learning_map = {
+            0: ignore_index,
+            1: ignore_index,
+            2: 6,
+            3: 6,
+            4: 6,
+            5: ignore_index,
+            6: 6,
+            7: ignore_index,
+            8: ignore_index,
+            9: 0,
+            10: ignore_index,
+            11: ignore_index,
+            12: 7,
+            13: ignore_index,
+            14: 1,
+            15: 2,
+            16: 2,
+            17: 3,
+            18: 4,
+            19: ignore_index,
+            20: ignore_index,
+            21: 5,
+            22: 8,
+            23: 9,
+            24: 10,
+            25: 11,
+            26: 12,
+            27: 13,
+            28: 14,
+            29: ignore_index,
+            30: 15,
+            31: ignore_index,
+        }
+        return learning_map

code/pointcept/datasets/preprocessing/arkitscenes/preprocess_arkitscenes_mesh.py

@@ -0,0 +1,87 @@
+"""
+Preprocessing ArkitScenes
+"""
+
+import os
+import argparse
+import glob
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+import torch
+
+
+def read_plymesh(filepath):
+    """Read ply file and return it as numpy array. Returns None if emtpy."""
+    with open(filepath, "rb") as f:
+        plydata = plyfile.PlyData.read(f)
+    if plydata.elements:
+        vertices = pd.DataFrame(plydata["vertex"].data).values
+        faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+        return vertices, faces
+
+
+def face_normal(vertex, face):
+    v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+    v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+    vec = np.cross(v01, v02)
+    length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+    nf = vec / length
+    area = length * 0.5
+    return nf, area
+
+
+def vertex_normal(vertex, face):
+    nf, area = face_normal(vertex, face)
+    nf = nf * area
+
+    nv = np.zeros_like(vertex)
+    for i in range(face.shape[0]):
+        nv[face[i]] += nf[i]
+
+    length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+    nv = nv / length
+    return nv
+
+
+def parse_scene(scene_path, output_dir):
+    print(f"Parsing scene {scene_path}")
+    split = os.path.basename(os.path.dirname(os.path.dirname(scene_path)))
+    scene_id = os.path.basename(os.path.dirname(scene_path))
+    vertices, faces = read_plymesh(scene_path)
+    coords = vertices[:, :3]
+    colors = vertices[:, 3:6]
+    data_dict = dict(coord=coords, color=colors, scene_id=scene_id)
+    data_dict["normal"] = vertex_normal(coords, faces)
+    torch.save(data_dict, os.path.join(output_dir, split, f"{scene_id}.pth"))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet dataset containing scene folders",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located",
+    )
+    opt = parser.parse_args()
+    # Create output directories
+    train_output_dir = os.path.join(opt.output_root, "Training")
+    os.makedirs(train_output_dir, exist_ok=True)
+    val_output_dir = os.path.join(opt.output_root, "Validation")
+    os.makedirs(val_output_dir, exist_ok=True)
+    # Load scene paths
+    scene_paths = sorted(glob.glob(opt.dataset_root + "/3dod/*/*/*_mesh.ply"))
+    # Preprocess data.
+    pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+    # pool = ProcessPoolExecutor(max_workers=1)
+    print("Processing scenes...")
+    _ = list(pool.map(parse_scene, scene_paths, repeat(opt.output_root)))

code/pointcept/datasets/preprocessing/nuscenes/preprocess_nuscenes_info.py

@@ -0,0 +1,607 @@
+"""
+Preprocessing Script for nuScenes Informantion
+modified from OpenPCDet (https://github.com/open-mmlab/OpenPCDet)
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+from pathlib import Path
+import numpy as np
+import argparse
+import tqdm
+import pickle
+from functools import reduce
+from pyquaternion import Quaternion
+from nuscenes.nuscenes import NuScenes
+from nuscenes.utils import splits
+from nuscenes.utils.geometry_utils import transform_matrix
+
+
+map_name_from_general_to_detection = {
+    "human.pedestrian.adult": "pedestrian",
+    "human.pedestrian.child": "pedestrian",
+    "human.pedestrian.wheelchair": "ignore",
+    "human.pedestrian.stroller": "ignore",
+    "human.pedestrian.personal_mobility": "ignore",
+    "human.pedestrian.police_officer": "pedestrian",
+    "human.pedestrian.construction_worker": "pedestrian",
+    "animal": "ignore",
+    "vehicle.car": "car",
+    "vehicle.motorcycle": "motorcycle",
+    "vehicle.bicycle": "bicycle",
+    "vehicle.bus.bendy": "bus",
+    "vehicle.bus.rigid": "bus",
+    "vehicle.truck": "truck",
+    "vehicle.construction": "construction_vehicle",
+    "vehicle.emergency.ambulance": "ignore",
+    "vehicle.emergency.police": "ignore",
+    "vehicle.trailer": "trailer",
+    "movable_object.barrier": "barrier",
+    "movable_object.trafficcone": "traffic_cone",
+    "movable_object.pushable_pullable": "ignore",
+    "movable_object.debris": "ignore",
+    "static_object.bicycle_rack": "ignore",
+}
+
+
+cls_attr_dist = {
+    "barrier": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "bicycle": {
+        "cycle.with_rider": 2791,
+        "cycle.without_rider": 8946,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "bus": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 9092,
+        "vehicle.parked": 3294,
+        "vehicle.stopped": 3881,
+    },
+    "car": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 114304,
+        "vehicle.parked": 330133,
+        "vehicle.stopped": 46898,
+    },
+    "construction_vehicle": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 882,
+        "vehicle.parked": 11549,
+        "vehicle.stopped": 2102,
+    },
+    "ignore": {
+        "cycle.with_rider": 307,
+        "cycle.without_rider": 73,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 165,
+        "vehicle.parked": 400,
+        "vehicle.stopped": 102,
+    },
+    "motorcycle": {
+        "cycle.with_rider": 4233,
+        "cycle.without_rider": 8326,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "pedestrian": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 157444,
+        "pedestrian.sitting_lying_down": 13939,
+        "pedestrian.standing": 46530,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "traffic_cone": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 0,
+        "vehicle.parked": 0,
+        "vehicle.stopped": 0,
+    },
+    "trailer": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 3421,
+        "vehicle.parked": 19224,
+        "vehicle.stopped": 1895,
+    },
+    "truck": {
+        "cycle.with_rider": 0,
+        "cycle.without_rider": 0,
+        "pedestrian.moving": 0,
+        "pedestrian.sitting_lying_down": 0,
+        "pedestrian.standing": 0,
+        "vehicle.moving": 21339,
+        "vehicle.parked": 55626,
+        "vehicle.stopped": 11097,
+    },
+}
+
+
+def get_available_scenes(nusc):
+    available_scenes = []
+    for scene in nusc.scene:
+        scene_token = scene["token"]
+        scene_rec = nusc.get("scene", scene_token)
+        sample_rec = nusc.get("sample", scene_rec["first_sample_token"])
+        sd_rec = nusc.get("sample_data", sample_rec["data"]["LIDAR_TOP"])
+        has_more_frames = True
+        scene_not_exist = False
+        while has_more_frames:
+            lidar_path, boxes, _ = nusc.get_sample_data(sd_rec["token"])
+            if not Path(lidar_path).exists():
+                scene_not_exist = True
+                break
+            else:
+                break
+        if scene_not_exist:
+            continue
+        available_scenes.append(scene)
+    return available_scenes
+
+
+def get_sample_data(nusc, sample_data_token, selected_anntokens=None):
+    """
+    Returns the data path as well as all annotations related to that sample_data.
+    Note that the boxes are transformed into the current sensor"s coordinate frame.
+    Args:
+        nusc:
+        sample_data_token: Sample_data token.
+        selected_anntokens: If provided only return the selected annotation.
+
+    Returns:
+
+    """
+    # Retrieve sensor & pose records
+    sd_record = nusc.get("sample_data", sample_data_token)
+    cs_record = nusc.get("calibrated_sensor", sd_record["calibrated_sensor_token"])
+    sensor_record = nusc.get("sensor", cs_record["sensor_token"])
+    pose_record = nusc.get("ego_pose", sd_record["ego_pose_token"])
+
+    data_path = nusc.get_sample_data_path(sample_data_token)
+
+    if sensor_record["modality"] == "camera":
+        cam_intrinsic = np.array(cs_record["camera_intrinsic"])
+    else:
+        cam_intrinsic = None
+
+    # Retrieve all sample annotations and map to sensor coordinate system.
+    if selected_anntokens is not None:
+        boxes = list(map(nusc.get_box, selected_anntokens))
+    else:
+        boxes = nusc.get_boxes(sample_data_token)
+
+    # Make list of Box objects including coord system transforms.
+    box_list = []
+    for box in boxes:
+        box.velocity = nusc.box_velocity(box.token)
+        # Move box to ego vehicle coord system
+        box.translate(-np.array(pose_record["translation"]))
+        box.rotate(Quaternion(pose_record["rotation"]).inverse)
+
+        #  Move box to sensor coord system
+        box.translate(-np.array(cs_record["translation"]))
+        box.rotate(Quaternion(cs_record["rotation"]).inverse)
+
+        box_list.append(box)
+
+    return data_path, box_list, cam_intrinsic
+
+
+def quaternion_yaw(q: Quaternion) -> float:
+    """
+    Calculate the yaw angle from a quaternion.
+    Note that this only works for a quaternion that represents a box in lidar or global coordinate frame.
+    It does not work for a box in the camera frame.
+    :param q: Quaternion of interest.
+    :return: Yaw angle in radians.
+    """
+
+    # Project into xy plane.
+    v = np.dot(q.rotation_matrix, np.array([1, 0, 0]))
+
+    # Measure yaw using arctan.
+    yaw = np.arctan2(v[1], v[0])
+
+    return yaw
+
+
+def obtain_sensor2top(
+    nusc, sensor_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, sensor_type="lidar"
+):
+    """Obtain the info with RT matric from general sensor to Top LiDAR.
+
+    Args:
+        nusc (class): Dataset class in the nuScenes dataset.
+        sensor_token (str): Sample data token corresponding to the
+            specific sensor type.
+        l2e_t (np.ndarray): Translation from lidar to ego in shape (1, 3).
+        l2e_r_mat (np.ndarray): Rotation matrix from lidar to ego
+            in shape (3, 3).
+        e2g_t (np.ndarray): Translation from ego to global in shape (1, 3).
+        e2g_r_mat (np.ndarray): Rotation matrix from ego to global
+            in shape (3, 3).
+        sensor_type (str): Sensor to calibrate. Default: "lidar".
+
+    Returns:
+        sweep (dict): Sweep information after transformation.
+    """
+    sd_rec = nusc.get("sample_data", sensor_token)
+    cs_record = nusc.get("calibrated_sensor", sd_rec["calibrated_sensor_token"])
+    pose_record = nusc.get("ego_pose", sd_rec["ego_pose_token"])
+    data_path = str(nusc.get_sample_data_path(sd_rec["token"]))
+    # if os.getcwd() in data_path:  # path from lyftdataset is absolute path
+    #     data_path = data_path.split(f"{os.getcwd()}/")[-1]  # relative path
+    sweep = {
+        "data_path": data_path,
+        "type": sensor_type,
+        "sample_data_token": sd_rec["token"],
+        "sensor2ego_translation": cs_record["translation"],
+        "sensor2ego_rotation": cs_record["rotation"],
+        "ego2global_translation": pose_record["translation"],
+        "ego2global_rotation": pose_record["rotation"],
+        "timestamp": sd_rec["timestamp"],
+    }
+    l2e_r_s = sweep["sensor2ego_rotation"]
+    l2e_t_s = sweep["sensor2ego_translation"]
+    e2g_r_s = sweep["ego2global_rotation"]
+    e2g_t_s = sweep["ego2global_translation"]
+
+    # obtain the RT from sensor to Top LiDAR
+    # sweep->ego->global->ego'->lidar
+    l2e_r_s_mat = Quaternion(l2e_r_s).rotation_matrix
+    e2g_r_s_mat = Quaternion(e2g_r_s).rotation_matrix
+    R = (l2e_r_s_mat.T @ e2g_r_s_mat.T) @ (
+        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+    )
+    T = (l2e_t_s @ e2g_r_s_mat.T + e2g_t_s) @ (
+        np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T
+    )
+    T -= (
+        e2g_t @ (np.linalg.inv(e2g_r_mat).T @ np.linalg.inv(l2e_r_mat).T)
+        + l2e_t @ np.linalg.inv(l2e_r_mat).T
+    ).squeeze(0)
+    sweep["sensor2lidar_rotation"] = R.T  # points @ R.T + T
+    sweep["sensor2lidar_translation"] = T
+    return sweep
+
+
+def fill_trainval_infos(
+    data_path, nusc, train_scenes, test=False, max_sweeps=10, with_camera=False
+):
+    train_nusc_infos = []
+    val_nusc_infos = []
+    progress_bar = tqdm.tqdm(
+        total=len(nusc.sample), desc="create_info", dynamic_ncols=True
+    )
+
+    ref_chan = "LIDAR_TOP"  # The radar channel from which we track back n sweeps to aggregate the point cloud.
+    chan = "LIDAR_TOP"  # The reference channel of the current sample_rec that the point clouds are mapped to.
+
+    for index, sample in enumerate(nusc.sample):
+        progress_bar.update()
+
+        ref_sd_token = sample["data"][ref_chan]
+        ref_sd_rec = nusc.get("sample_data", ref_sd_token)
+        ref_cs_rec = nusc.get(
+            "calibrated_sensor", ref_sd_rec["calibrated_sensor_token"]
+        )
+        ref_pose_rec = nusc.get("ego_pose", ref_sd_rec["ego_pose_token"])
+        ref_time = 1e-6 * ref_sd_rec["timestamp"]
+
+        ref_lidar_path, ref_boxes, _ = get_sample_data(nusc, ref_sd_token)
+
+        ref_cam_front_token = sample["data"]["CAM_FRONT"]
+        ref_cam_path, _, ref_cam_intrinsic = nusc.get_sample_data(ref_cam_front_token)
+
+        # Homogeneous transform from ego car frame to reference frame
+        ref_from_car = transform_matrix(
+            ref_cs_rec["translation"], Quaternion(ref_cs_rec["rotation"]), inverse=True
+        )
+
+        # Homogeneous transformation matrix from global to _current_ ego car frame
+        car_from_global = transform_matrix(
+            ref_pose_rec["translation"],
+            Quaternion(ref_pose_rec["rotation"]),
+            inverse=True,
+        )
+        info = {
+            "lidar_path": Path(ref_lidar_path).relative_to(data_path).__str__(),
+            "lidar_token": ref_sd_token,
+            "cam_front_path": Path(ref_cam_path).relative_to(data_path).__str__(),
+            "cam_intrinsic": ref_cam_intrinsic,
+            "token": sample["token"],
+            "sweeps": [],
+            "ref_from_car": ref_from_car,
+            "car_from_global": car_from_global,
+            "timestamp": ref_time,
+        }
+        if with_camera:
+            info["cams"] = dict()
+            l2e_r = ref_cs_rec["rotation"]
+            l2e_t = (ref_cs_rec["translation"],)
+            e2g_r = ref_pose_rec["rotation"]
+            e2g_t = ref_pose_rec["translation"]
+            l2e_r_mat = Quaternion(l2e_r).rotation_matrix
+            e2g_r_mat = Quaternion(e2g_r).rotation_matrix
+
+            # obtain 6 image's information per frame
+            camera_types = [
+                "CAM_FRONT",
+                "CAM_FRONT_RIGHT",
+                "CAM_FRONT_LEFT",
+                "CAM_BACK",
+                "CAM_BACK_LEFT",
+                "CAM_BACK_RIGHT",
+            ]
+            for cam in camera_types:
+                cam_token = sample["data"][cam]
+                cam_path, _, camera_intrinsics = nusc.get_sample_data(cam_token)
+                cam_info = obtain_sensor2top(
+                    nusc, cam_token, l2e_t, l2e_r_mat, e2g_t, e2g_r_mat, cam
+                )
+                cam_info["data_path"] = (
+                    Path(cam_info["data_path"]).relative_to(data_path).__str__()
+                )
+                cam_info.update(camera_intrinsics=camera_intrinsics)
+                info["cams"].update({cam: cam_info})
+
+        sample_data_token = sample["data"][chan]
+        curr_sd_rec = nusc.get("sample_data", sample_data_token)
+        sweeps = []
+        while len(sweeps) < max_sweeps - 1:
+            if curr_sd_rec["prev"] == "":
+                if len(sweeps) == 0:
+                    sweep = {
+                        "lidar_path": Path(ref_lidar_path)
+                        .relative_to(data_path)
+                        .__str__(),
+                        "sample_data_token": curr_sd_rec["token"],
+                        "transform_matrix": None,
+                        "time_lag": curr_sd_rec["timestamp"] * 0,
+                    }
+                    sweeps.append(sweep)
+                else:
+                    sweeps.append(sweeps[-1])
+            else:
+                curr_sd_rec = nusc.get("sample_data", curr_sd_rec["prev"])
+
+                # Get past pose
+                current_pose_rec = nusc.get("ego_pose", curr_sd_rec["ego_pose_token"])
+                global_from_car = transform_matrix(
+                    current_pose_rec["translation"],
+                    Quaternion(current_pose_rec["rotation"]),
+                    inverse=False,
+                )
+
+                # Homogeneous transformation matrix from sensor coordinate frame to ego car frame.
+                current_cs_rec = nusc.get(
+                    "calibrated_sensor", curr_sd_rec["calibrated_sensor_token"]
+                )
+                car_from_current = transform_matrix(
+                    current_cs_rec["translation"],
+                    Quaternion(current_cs_rec["rotation"]),
+                    inverse=False,
+                )
+
+                tm = reduce(
+                    np.dot,
+                    [ref_from_car, car_from_global, global_from_car, car_from_current],
+                )
+
+                lidar_path = nusc.get_sample_data_path(curr_sd_rec["token"])
+
+                time_lag = ref_time - 1e-6 * curr_sd_rec["timestamp"]
+
+                sweep = {
+                    "lidar_path": Path(lidar_path).relative_to(data_path).__str__(),
+                    "sample_data_token": curr_sd_rec["token"],
+                    "transform_matrix": tm,
+                    "global_from_car": global_from_car,
+                    "car_from_current": car_from_current,
+                    "time_lag": time_lag,
+                }
+                sweeps.append(sweep)
+
+        info["sweeps"] = sweeps
+
+        assert len(info["sweeps"]) == max_sweeps - 1, (
+            f"sweep {curr_sd_rec['token']} only has {len(info['sweeps'])} sweeps, "
+            f"you should duplicate to sweep num {max_sweeps - 1}"
+        )
+
+        if not test:
+            # processing gt bbox
+            annotations = [
+                nusc.get("sample_annotation", token) for token in sample["anns"]
+            ]
+
+            # the filtering gives 0.5~1 map improvement
+            num_lidar_pts = np.array([anno["num_lidar_pts"] for anno in annotations])
+            num_radar_pts = np.array([anno["num_radar_pts"] for anno in annotations])
+            mask = num_lidar_pts + num_radar_pts > 0
+
+            locs = np.array([b.center for b in ref_boxes]).reshape(-1, 3)
+            dims = np.array([b.wlh for b in ref_boxes]).reshape(-1, 3)[
+                :, [1, 0, 2]
+            ]  # wlh == > dxdydz (lwh)
+            velocity = np.array([b.velocity for b in ref_boxes]).reshape(-1, 3)
+            rots = np.array([quaternion_yaw(b.orientation) for b in ref_boxes]).reshape(
+                -1, 1
+            )
+            names = np.array([b.name for b in ref_boxes])
+            tokens = np.array([b.token for b in ref_boxes])
+            gt_boxes = np.concatenate([locs, dims, rots, velocity[:, :2]], axis=1)
+
+            assert len(annotations) == len(gt_boxes) == len(velocity)
+
+            info["gt_boxes"] = gt_boxes[mask, :]
+            info["gt_boxes_velocity"] = velocity[mask, :]
+            info["gt_names"] = np.array(
+                [map_name_from_general_to_detection[name] for name in names]
+            )[mask]
+            info["gt_boxes_token"] = tokens[mask]
+            info["num_lidar_pts"] = num_lidar_pts[mask]
+            info["num_radar_pts"] = num_radar_pts[mask]
+
+            # processing gt segment
+            segment_path = nusc.get("lidarseg", ref_sd_token)["filename"]
+            info["gt_segment_path"] = segment_path
+
+        if sample["scene_token"] in train_scenes:
+            train_nusc_infos.append(info)
+        else:
+            val_nusc_infos.append(info)
+
+    progress_bar.close()
+    return train_nusc_infos, val_nusc_infos
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root", required=True, help="Path to the nuScenes dataset."
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where processed information located.",
+    )
+    parser.add_argument(
+        "--max_sweeps", default=10, type=int, help="Max number of sweeps. Default: 10."
+    )
+    parser.add_argument(
+        "--with_camera",
+        action="store_true",
+        default=False,
+        help="Whether use camera or not.",
+    )
+    config = parser.parse_args()
+
+    print(f"Loading nuScenes tables for version v1.0-trainval...")
+    nusc_trainval = NuScenes(
+        version="v1.0-trainval", dataroot=config.dataset_root, verbose=False
+    )
+    available_scenes_trainval = get_available_scenes(nusc_trainval)
+    available_scene_names_trainval = [s["name"] for s in available_scenes_trainval]
+    print("total scene num:", len(nusc_trainval.scene))
+    print("exist scene num:", len(available_scenes_trainval))
+    assert len(available_scenes_trainval) == len(nusc_trainval.scene) == 850
+
+    print(f"Loading nuScenes tables for version v1.0-test...")
+    nusc_test = NuScenes(
+        version="v1.0-test", dataroot=config.dataset_root, verbose=False
+    )
+    available_scenes_test = get_available_scenes(nusc_test)
+    available_scene_names_test = [s["name"] for s in available_scenes_test]
+    print("total scene num:", len(nusc_test.scene))
+    print("exist scene num:", len(available_scenes_test))
+    assert len(available_scenes_test) == len(nusc_test.scene) == 150
+
+    train_scenes = splits.train
+    train_scenes = set(
+        [
+            available_scenes_trainval[available_scene_names_trainval.index(s)]["token"]
+            for s in train_scenes
+        ]
+    )
+    test_scenes = splits.test
+    test_scenes = set(
+        [
+            available_scenes_test[available_scene_names_test.index(s)]["token"]
+            for s in test_scenes
+        ]
+    )
+    print(f"Filling trainval information...")
+    train_nusc_infos, val_nusc_infos = fill_trainval_infos(
+        config.dataset_root,
+        nusc_trainval,
+        train_scenes,
+        test=False,
+        max_sweeps=config.max_sweeps,
+        with_camera=config.with_camera,
+    )
+    print(f"Filling test information...")
+    test_nusc_infos, _ = fill_trainval_infos(
+        config.dataset_root,
+        nusc_test,
+        test_scenes,
+        test=True,
+        max_sweeps=config.max_sweeps,
+        with_camera=config.with_camera,
+    )
+
+    print(f"Saving nuScenes information...")
+    os.makedirs(os.path.join(config.output_root, "info"), exist_ok=True)
+    print(
+        f"train sample: {len(train_nusc_infos)}, val sample: {len(val_nusc_infos)}, test sample: {len(test_nusc_infos)}"
+    )
+    with open(
+        os.path.join(
+            config.output_root,
+            "info",
+            f"nuscenes_infos_{config.max_sweeps}sweeps_train.pkl",
+        ),
+        "wb",
+    ) as f:
+        pickle.dump(train_nusc_infos, f)
+    with open(
+        os.path.join(
+            config.output_root,
+            "info",
+            f"nuscenes_infos_{config.max_sweeps}sweeps_val.pkl",
+        ),
+        "wb",
+    ) as f:
+        pickle.dump(val_nusc_infos, f)
+    with open(
+        os.path.join(
+            config.output_root,
+            "info",
+            f"nuscenes_infos_{config.max_sweeps}sweeps_test.pkl",
+        ),
+        "wb",
+    ) as f:
+        pickle.dump(test_nusc_infos, f)

code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis.py

@@ -0,0 +1,234 @@
+"""
+Preprocessing Script for S3DIS
+Parsing normal vectors has a large consumption of memory. Please reduce max_workers if memory is limited.
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import glob
+import torch
+import numpy as np
+import multiprocessing as mp
+
+try:
+    import open3d
+except ImportError:
+    import warnings
+
+    warnings.warn("Please install open3d for parsing normal")
+
+try:
+    import trimesh
+except ImportError:
+    import warnings
+
+    warnings.warn("Please install trimesh for parsing normal")
+
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+area_mesh_dict = {}
+
+
+def parse_room(
+    room, angle, dataset_root, output_root, align_angle=True, parse_normal=False
+):
+    print("Parsing: {}".format(room))
+    classes = [
+        "ceiling",
+        "floor",
+        "wall",
+        "beam",
+        "column",
+        "window",
+        "door",
+        "table",
+        "chair",
+        "sofa",
+        "bookcase",
+        "board",
+        "clutter",
+    ]
+    class2label = {cls: i for i, cls in enumerate(classes)}
+    source_dir = os.path.join(dataset_root, room)
+    save_path = os.path.join(output_root, room) + ".pth"
+    os.makedirs(os.path.dirname(save_path), exist_ok=True)
+    object_path_list = sorted(glob.glob(os.path.join(source_dir, "Annotations/*.txt")))
+
+    room_coords = []
+    room_colors = []
+    room_normals = []
+    room_semantic_gt = []
+    room_instance_gt = []
+
+    for object_id, object_path in enumerate(object_path_list):
+        object_name = os.path.basename(object_path).split("_")[0]
+        obj = np.loadtxt(object_path)
+        coords = obj[:, :3]
+        colors = obj[:, 3:6]
+        # note: in some room there is 'stairs' class
+        class_name = object_name if object_name in classes else "clutter"
+        semantic_gt = np.repeat(class2label[class_name], coords.shape[0])
+        semantic_gt = semantic_gt.reshape([-1, 1])
+        instance_gt = np.repeat(object_id, coords.shape[0])
+        instance_gt = instance_gt.reshape([-1, 1])
+
+        room_coords.append(coords)
+        room_colors.append(colors)
+        room_semantic_gt.append(semantic_gt)
+        room_instance_gt.append(instance_gt)
+
+    room_coords = np.ascontiguousarray(np.vstack(room_coords))
+
+    if parse_normal:
+        x_min, z_max, y_min = np.min(room_coords, axis=0)
+        x_max, z_min, y_max = np.max(room_coords, axis=0)
+        z_max = -z_max
+        z_min = -z_min
+        max_bound = np.array([x_max, y_max, z_max]) + 0.1
+        min_bound = np.array([x_min, y_min, z_min]) - 0.1
+        bbox = open3d.geometry.AxisAlignedBoundingBox(
+            min_bound=min_bound, max_bound=max_bound
+        )
+        # crop room
+        room_mesh = (
+            area_mesh_dict[os.path.dirname(room)]
+            .crop(bbox)
+            .transform(
+                np.array([[1, 0, 0, 0], [0, 0, -1, 0], [0, 1, 0, 0], [0, 0, 0, 1]])
+            )
+        )
+        vertices = np.array(room_mesh.vertices)
+        faces = np.array(room_mesh.triangles)
+        vertex_normals = np.array(room_mesh.vertex_normals)
+        room_mesh = trimesh.Trimesh(
+            vertices=vertices, faces=faces, vertex_normals=vertex_normals
+        )
+        (closest_points, distances, face_id) = room_mesh.nearest.on_surface(room_coords)
+        room_normals = room_mesh.face_normals[face_id]
+
+    if align_angle:
+        angle = (2 - angle / 180) * np.pi
+        rot_cos, rot_sin = np.cos(angle), np.sin(angle)
+        rot_t = np.array([[rot_cos, -rot_sin, 0], [rot_sin, rot_cos, 0], [0, 0, 1]])
+        room_center = (np.max(room_coords, axis=0) + np.min(room_coords, axis=0)) / 2
+        room_coords = (room_coords - room_center) @ np.transpose(rot_t) + room_center
+        if parse_normal:
+            room_normals = room_normals @ np.transpose(rot_t)
+
+    room_colors = np.ascontiguousarray(np.vstack(room_colors))
+    room_semantic_gt = np.ascontiguousarray(np.vstack(room_semantic_gt))
+    room_instance_gt = np.ascontiguousarray(np.vstack(room_instance_gt))
+    save_dict = dict(
+        coord=room_coords,
+        color=room_colors,
+        semantic_gt=room_semantic_gt,
+        instance_gt=room_instance_gt,
+    )
+    if parse_normal:
+        save_dict["normal"] = room_normals
+    torch.save(save_dict, save_path)
+
+
+def main_process():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root", required=True, help="Path to Stanford3dDataset_v1.2 dataset"
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where area folders will be located",
+    )
+    parser.add_argument(
+        "--raw_root",
+        default=None,
+        help="Path to Stanford2d3dDataset_noXYZ dataset (optional)",
+    )
+    parser.add_argument(
+        "--align_angle", action="store_true", help="Whether align room angles"
+    )
+    parser.add_argument(
+        "--parse_normal", action="store_true", help="Whether process normal"
+    )
+    args = parser.parse_args()
+
+    if args.parse_normal:
+        assert args.raw_root is not None
+
+    room_list = []
+    angle_list = []
+
+    # Load room information
+    print("Loading room information ...")
+    for i in range(1, 7):
+        area_info = np.loadtxt(
+            os.path.join(
+                args.dataset_root,
+                "Area_{}".format(i),
+                "Area_{}_alignmentAngle.txt".format(i),
+            ),
+            dtype=str,
+        )
+        room_list += [
+            os.path.join("Area_{}".format(i), room_info[0]) for room_info in area_info
+        ]
+        angle_list += [int(room_info[1]) for room_info in area_info]
+
+    if args.parse_normal:
+        # load raw mesh file to extract normal
+        print("Loading raw mesh file ...")
+        for i in range(1, 7):
+            if i != 5:
+                mesh_dir = os.path.join(
+                    args.raw_root, "area_{}".format(i), "3d", "rgb.obj"
+                )
+                mesh = open3d.io.read_triangle_mesh(mesh_dir)
+                mesh.triangle_uvs.clear()
+            else:
+                mesh_a_dir = os.path.join(
+                    args.raw_root, "area_{}a".format(i), "3d", "rgb.obj"
+                )
+                mesh_b_dir = os.path.join(
+                    args.raw_root, "area_{}b".format(i), "3d", "rgb.obj"
+                )
+                mesh_a = open3d.io.read_triangle_mesh(mesh_a_dir)
+                mesh_a.triangle_uvs.clear()
+                mesh_b = open3d.io.read_triangle_mesh(mesh_b_dir)
+                mesh_b.triangle_uvs.clear()
+                mesh_b = mesh_b.transform(
+                    np.array(
+                        [
+                            [0, 0, -1, -4.09703582],
+                            [0, 1, 0, 0],
+                            [1, 0, 0, -6.22617759],
+                            [0, 0, 0, 1],
+                        ]
+                    )
+                )
+                mesh = mesh_a + mesh_b
+            area_mesh_dict["Area_{}".format(i)] = mesh
+            print("Area_{} mesh is loaded".format(i))
+
+    # Preprocess data.
+    print("Processing scenes...")
+    # pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+    pool = ProcessPoolExecutor(max_workers=8)  # peak 110G memory when parsing normal.
+    _ = list(
+        pool.map(
+            parse_room,
+            room_list,
+            angle_list,
+            repeat(args.dataset_root),
+            repeat(args.output_root),
+            repeat(args.align_angle),
+            repeat(args.parse_normal),
+        )
+    )
+
+
+if __name__ == "__main__":
+    main_process()

code/pointcept/datasets/preprocessing/s3dis/preprocess_s3dis_voxelized.py

@@ -0,0 +1,64 @@
+"""
+Preprocessing Script for S3DIS
+Parsing normal vectors has a large consumption of memory. Please reduce max_workers if memory is limited.
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import os
+import argparse
+import glob
+import torch
+import numpy as np
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+from pointcept.datasets.transform import GridSample
+
+
+def voxelize_parser(data_path, dataset_root, output_root, voxel_size):
+    print(f"Parsing data: {data_path}")
+    out_path = data_path.replace(dataset_root, output_root)
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    data = torch.load(data_path)
+    data = GridSample(
+        grid_size=voxel_size, hash_type="fnv", mode="train", keys=data.keys()
+    )(data)
+    torch.save(data, out_path)
+
+
+def main_process():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root", required=True, help="Path to processed S3DIS dataset"
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where area folders will be located",
+    )
+    parser.add_argument(
+        "--voxel_size", default=0.01, type=float, help="Voxel size for voxelization"
+    )
+    args = parser.parse_args()
+
+    data_list = glob.glob(os.path.join(args.dataset_root, "*/*.pth"))
+    # Preprocess data.
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+    # pool = ProcessPoolExecutor(max_workers=1)
+    _ = list(
+        pool.map(
+            voxelize_parser,
+            data_list,
+            repeat(args.dataset_root),
+            repeat(args.output_root),
+            repeat(args.voxel_size),
+        )
+    )
+
+
+if __name__ == "__main__":
+    main_process()

code/pointcept/datasets/preprocessing/scannet/meta_data/classes_ObjClassification-ShapeNetCore55.txt

@@ -0,0 +1,17 @@
+1	trash
+3 	basket
+4	bathtub
+5	bed
+9	shelf
+13	cabinet
+18	chair
+20	keyboard
+22	tv
+30	lamp
+31	laptop
+35	microwave
+39	pillow
+42	printer
+47	sofa
+48	stove
+49	table

code/pointcept/datasets/preprocessing/scannet/meta_data/classes_SemVoxLabel-nyu40id.txt

@@ -0,0 +1,20 @@
+1       wall
+2       floor
+3       cabinet
+4       bed
+5       chair
+6       sofa
+7       table
+8       door
+9       window
+10      bookshelf
+11      picture
+12      counter
+14      desk
+16      curtain
+24      refridgerator
+28      shower curtain
+33      toilet
+34      sink
+36      bathtub
+39      otherfurniture

code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_constants.py

@@ -0,0 +1,704 @@
+# ScanNet Benchmark constants
+VALID_CLASS_IDS_20 = (
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    14,
+    16,
+    24,
+    28,
+    33,
+    34,
+    36,
+    39,
+)
+
+CLASS_LABELS_20 = (
+    "wall",
+    "floor",
+    "cabinet",
+    "bed",
+    "chair",
+    "sofa",
+    "table",
+    "door",
+    "window",
+    "bookshelf",
+    "picture",
+    "counter",
+    "desk",
+    "curtain",
+    "refrigerator",
+    "shower curtain",
+    "toilet",
+    "sink",
+    "bathtub",
+    "otherfurniture",
+)
+
+SCANNET_COLOR_MAP_20 = {
+    0: (0.0, 0.0, 0.0),
+    1: (174.0, 199.0, 232.0),
+    2: (152.0, 223.0, 138.0),
+    3: (31.0, 119.0, 180.0),
+    4: (255.0, 187.0, 120.0),
+    5: (188.0, 189.0, 34.0),
+    6: (140.0, 86.0, 75.0),
+    7: (255.0, 152.0, 150.0),
+    8: (214.0, 39.0, 40.0),
+    9: (197.0, 176.0, 213.0),
+    10: (148.0, 103.0, 189.0),
+    11: (196.0, 156.0, 148.0),
+    12: (23.0, 190.0, 207.0),
+    14: (247.0, 182.0, 210.0),
+    15: (66.0, 188.0, 102.0),
+    16: (219.0, 219.0, 141.0),
+    17: (140.0, 57.0, 197.0),
+    18: (202.0, 185.0, 52.0),
+    19: (51.0, 176.0, 203.0),
+    20: (200.0, 54.0, 131.0),
+    21: (92.0, 193.0, 61.0),
+    22: (78.0, 71.0, 183.0),
+    23: (172.0, 114.0, 82.0),
+    24: (255.0, 127.0, 14.0),
+    25: (91.0, 163.0, 138.0),
+    26: (153.0, 98.0, 156.0),
+    27: (140.0, 153.0, 101.0),
+    28: (158.0, 218.0, 229.0),
+    29: (100.0, 125.0, 154.0),
+    30: (178.0, 127.0, 135.0),
+    32: (146.0, 111.0, 194.0),
+    33: (44.0, 160.0, 44.0),
+    34: (112.0, 128.0, 144.0),
+    35: (96.0, 207.0, 209.0),
+    36: (227.0, 119.0, 194.0),
+    37: (213.0, 92.0, 176.0),
+    38: (94.0, 106.0, 211.0),
+    39: (82.0, 84.0, 163.0),
+    40: (100.0, 85.0, 144.0),
+}
+
+# ScanNet200 Benchmark constants
+VALID_CLASS_IDS_200 = (
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    21,
+    22,
+    23,
+    24,
+    26,
+    27,
+    28,
+    29,
+    31,
+    32,
+    33,
+    34,
+    35,
+    36,
+    38,
+    39,
+    40,
+    41,
+    42,
+    44,
+    45,
+    46,
+    47,
+    48,
+    49,
+    50,
+    51,
+    52,
+    54,
+    55,
+    56,
+    57,
+    58,
+    59,
+    62,
+    63,
+    64,
+    65,
+    66,
+    67,
+    68,
+    69,
+    70,
+    71,
+    72,
+    73,
+    74,
+    75,
+    76,
+    77,
+    78,
+    79,
+    80,
+    82,
+    84,
+    86,
+    87,
+    88,
+    89,
+    90,
+    93,
+    95,
+    96,
+    97,
+    98,
+    99,
+    100,
+    101,
+    102,
+    103,
+    104,
+    105,
+    106,
+    107,
+    110,
+    112,
+    115,
+    116,
+    118,
+    120,
+    121,
+    122,
+    125,
+    128,
+    130,
+    131,
+    132,
+    134,
+    136,
+    138,
+    139,
+    140,
+    141,
+    145,
+    148,
+    154,
+    155,
+    156,
+    157,
+    159,
+    161,
+    163,
+    165,
+    166,
+    168,
+    169,
+    170,
+    177,
+    180,
+    185,
+    188,
+    191,
+    193,
+    195,
+    202,
+    208,
+    213,
+    214,
+    221,
+    229,
+    230,
+    232,
+    233,
+    242,
+    250,
+    261,
+    264,
+    276,
+    283,
+    286,
+    300,
+    304,
+    312,
+    323,
+    325,
+    331,
+    342,
+    356,
+    370,
+    392,
+    395,
+    399,
+    408,
+    417,
+    488,
+    540,
+    562,
+    570,
+    572,
+    581,
+    609,
+    748,
+    776,
+    1156,
+    1163,
+    1164,
+    1165,
+    1166,
+    1167,
+    1168,
+    1169,
+    1170,
+    1171,
+    1172,
+    1173,
+    1174,
+    1175,
+    1176,
+    1178,
+    1179,
+    1180,
+    1181,
+    1182,
+    1183,
+    1184,
+    1185,
+    1186,
+    1187,
+    1188,
+    1189,
+    1190,
+    1191,
+)
+
+CLASS_LABELS_200 = (
+    "wall",
+    "chair",
+    "floor",
+    "table",
+    "door",
+    "couch",
+    "cabinet",
+    "shelf",
+    "desk",
+    "office chair",
+    "bed",
+    "pillow",
+    "sink",
+    "picture",
+    "window",
+    "toilet",
+    "bookshelf",
+    "monitor",
+    "curtain",
+    "book",
+    "armchair",
+    "coffee table",
+    "box",
+    "refrigerator",
+    "lamp",
+    "kitchen cabinet",
+    "towel",
+    "clothes",
+    "tv",
+    "nightstand",
+    "counter",
+    "dresser",
+    "stool",
+    "cushion",
+    "plant",
+    "ceiling",
+    "bathtub",
+    "end table",
+    "dining table",
+    "keyboard",
+    "bag",
+    "backpack",
+    "toilet paper",
+    "printer",
+    "tv stand",
+    "whiteboard",
+    "blanket",
+    "shower curtain",
+    "trash can",
+    "closet",
+    "stairs",
+    "microwave",
+    "stove",
+    "shoe",
+    "computer tower",
+    "bottle",
+    "bin",
+    "ottoman",
+    "bench",
+    "board",
+    "washing machine",
+    "mirror",
+    "copier",
+    "basket",
+    "sofa chair",
+    "file cabinet",
+    "fan",
+    "laptop",
+    "shower",
+    "paper",
+    "person",
+    "paper towel dispenser",
+    "oven",
+    "blinds",
+    "rack",
+    "plate",
+    "blackboard",
+    "piano",
+    "suitcase",
+    "rail",
+    "radiator",
+    "recycling bin",
+    "container",
+    "wardrobe",
+    "soap dispenser",
+    "telephone",
+    "bucket",
+    "clock",
+    "stand",
+    "light",
+    "laundry basket",
+    "pipe",
+    "clothes dryer",
+    "guitar",
+    "toilet paper holder",
+    "seat",
+    "speaker",
+    "column",
+    "bicycle",
+    "ladder",
+    "bathroom stall",
+    "shower wall",
+    "cup",
+    "jacket",
+    "storage bin",
+    "coffee maker",
+    "dishwasher",
+    "paper towel roll",
+    "machine",
+    "mat",
+    "windowsill",
+    "bar",
+    "toaster",
+    "bulletin board",
+    "ironing board",
+    "fireplace",
+    "soap dish",
+    "kitchen counter",
+    "doorframe",
+    "toilet paper dispenser",
+    "mini fridge",
+    "fire extinguisher",
+    "ball",
+    "hat",
+    "shower curtain rod",
+    "water cooler",
+    "paper cutter",
+    "tray",
+    "shower door",
+    "pillar",
+    "ledge",
+    "toaster oven",
+    "mouse",
+    "toilet seat cover dispenser",
+    "furniture",
+    "cart",
+    "storage container",
+    "scale",
+    "tissue box",
+    "light switch",
+    "crate",
+    "power outlet",
+    "decoration",
+    "sign",
+    "projector",
+    "closet door",
+    "vacuum cleaner",
+    "candle",
+    "plunger",
+    "stuffed animal",
+    "headphones",
+    "dish rack",
+    "broom",
+    "guitar case",
+    "range hood",
+    "dustpan",
+    "hair dryer",
+    "water bottle",
+    "handicap bar",
+    "purse",
+    "vent",
+    "shower floor",
+    "water pitcher",
+    "mailbox",
+    "bowl",
+    "paper bag",
+    "alarm clock",
+    "music stand",
+    "projector screen",
+    "divider",
+    "laundry detergent",
+    "bathroom counter",
+    "object",
+    "bathroom vanity",
+    "closet wall",
+    "laundry hamper",
+    "bathroom stall door",
+    "ceiling light",
+    "trash bin",
+    "dumbbell",
+    "stair rail",
+    "tube",
+    "bathroom cabinet",
+    "cd case",
+    "closet rod",
+    "coffee kettle",
+    "structure",
+    "shower head",
+    "keyboard piano",
+    "case of water bottles",
+    "coat rack",
+    "storage organizer",
+    "folded chair",
+    "fire alarm",
+    "power strip",
+    "calendar",
+    "poster",
+    "potted plant",
+    "luggage",
+    "mattress",
+)
+
+SCANNET_COLOR_MAP_200 = {
+    0: (0.0, 0.0, 0.0),
+    1: (174.0, 199.0, 232.0),
+    2: (188.0, 189.0, 34.0),
+    3: (152.0, 223.0, 138.0),
+    4: (255.0, 152.0, 150.0),
+    5: (214.0, 39.0, 40.0),
+    6: (91.0, 135.0, 229.0),
+    7: (31.0, 119.0, 180.0),
+    8: (229.0, 91.0, 104.0),
+    9: (247.0, 182.0, 210.0),
+    10: (91.0, 229.0, 110.0),
+    11: (255.0, 187.0, 120.0),
+    13: (141.0, 91.0, 229.0),
+    14: (112.0, 128.0, 144.0),
+    15: (196.0, 156.0, 148.0),
+    16: (197.0, 176.0, 213.0),
+    17: (44.0, 160.0, 44.0),
+    18: (148.0, 103.0, 189.0),
+    19: (229.0, 91.0, 223.0),
+    21: (219.0, 219.0, 141.0),
+    22: (192.0, 229.0, 91.0),
+    23: (88.0, 218.0, 137.0),
+    24: (58.0, 98.0, 137.0),
+    26: (177.0, 82.0, 239.0),
+    27: (255.0, 127.0, 14.0),
+    28: (237.0, 204.0, 37.0),
+    29: (41.0, 206.0, 32.0),
+    31: (62.0, 143.0, 148.0),
+    32: (34.0, 14.0, 130.0),
+    33: (143.0, 45.0, 115.0),
+    34: (137.0, 63.0, 14.0),
+    35: (23.0, 190.0, 207.0),
+    36: (16.0, 212.0, 139.0),
+    38: (90.0, 119.0, 201.0),
+    39: (125.0, 30.0, 141.0),
+    40: (150.0, 53.0, 56.0),
+    41: (186.0, 197.0, 62.0),
+    42: (227.0, 119.0, 194.0),
+    44: (38.0, 100.0, 128.0),
+    45: (120.0, 31.0, 243.0),
+    46: (154.0, 59.0, 103.0),
+    47: (169.0, 137.0, 78.0),
+    48: (143.0, 245.0, 111.0),
+    49: (37.0, 230.0, 205.0),
+    50: (14.0, 16.0, 155.0),
+    51: (196.0, 51.0, 182.0),
+    52: (237.0, 80.0, 38.0),
+    54: (138.0, 175.0, 62.0),
+    55: (158.0, 218.0, 229.0),
+    56: (38.0, 96.0, 167.0),
+    57: (190.0, 77.0, 246.0),
+    58: (208.0, 49.0, 84.0),
+    59: (208.0, 193.0, 72.0),
+    62: (55.0, 220.0, 57.0),
+    63: (10.0, 125.0, 140.0),
+    64: (76.0, 38.0, 202.0),
+    65: (191.0, 28.0, 135.0),
+    66: (211.0, 120.0, 42.0),
+    67: (118.0, 174.0, 76.0),
+    68: (17.0, 242.0, 171.0),
+    69: (20.0, 65.0, 247.0),
+    70: (208.0, 61.0, 222.0),
+    71: (162.0, 62.0, 60.0),
+    72: (210.0, 235.0, 62.0),
+    73: (45.0, 152.0, 72.0),
+    74: (35.0, 107.0, 149.0),
+    75: (160.0, 89.0, 237.0),
+    76: (227.0, 56.0, 125.0),
+    77: (169.0, 143.0, 81.0),
+    78: (42.0, 143.0, 20.0),
+    79: (25.0, 160.0, 151.0),
+    80: (82.0, 75.0, 227.0),
+    82: (253.0, 59.0, 222.0),
+    84: (240.0, 130.0, 89.0),
+    86: (123.0, 172.0, 47.0),
+    87: (71.0, 194.0, 133.0),
+    88: (24.0, 94.0, 205.0),
+    89: (134.0, 16.0, 179.0),
+    90: (159.0, 32.0, 52.0),
+    93: (213.0, 208.0, 88.0),
+    95: (64.0, 158.0, 70.0),
+    96: (18.0, 163.0, 194.0),
+    97: (65.0, 29.0, 153.0),
+    98: (177.0, 10.0, 109.0),
+    99: (152.0, 83.0, 7.0),
+    100: (83.0, 175.0, 30.0),
+    101: (18.0, 199.0, 153.0),
+    102: (61.0, 81.0, 208.0),
+    103: (213.0, 85.0, 216.0),
+    104: (170.0, 53.0, 42.0),
+    105: (161.0, 192.0, 38.0),
+    106: (23.0, 241.0, 91.0),
+    107: (12.0, 103.0, 170.0),
+    110: (151.0, 41.0, 245.0),
+    112: (133.0, 51.0, 80.0),
+    115: (184.0, 162.0, 91.0),
+    116: (50.0, 138.0, 38.0),
+    118: (31.0, 237.0, 236.0),
+    120: (39.0, 19.0, 208.0),
+    121: (223.0, 27.0, 180.0),
+    122: (254.0, 141.0, 85.0),
+    125: (97.0, 144.0, 39.0),
+    128: (106.0, 231.0, 176.0),
+    130: (12.0, 61.0, 162.0),
+    131: (124.0, 66.0, 140.0),
+    132: (137.0, 66.0, 73.0),
+    134: (250.0, 253.0, 26.0),
+    136: (55.0, 191.0, 73.0),
+    138: (60.0, 126.0, 146.0),
+    139: (153.0, 108.0, 234.0),
+    140: (184.0, 58.0, 125.0),
+    141: (135.0, 84.0, 14.0),
+    145: (139.0, 248.0, 91.0),
+    148: (53.0, 200.0, 172.0),
+    154: (63.0, 69.0, 134.0),
+    155: (190.0, 75.0, 186.0),
+    156: (127.0, 63.0, 52.0),
+    157: (141.0, 182.0, 25.0),
+    159: (56.0, 144.0, 89.0),
+    161: (64.0, 160.0, 250.0),
+    163: (182.0, 86.0, 245.0),
+    165: (139.0, 18.0, 53.0),
+    166: (134.0, 120.0, 54.0),
+    168: (49.0, 165.0, 42.0),
+    169: (51.0, 128.0, 133.0),
+    170: (44.0, 21.0, 163.0),
+    177: (232.0, 93.0, 193.0),
+    180: (176.0, 102.0, 54.0),
+    185: (116.0, 217.0, 17.0),
+    188: (54.0, 209.0, 150.0),
+    191: (60.0, 99.0, 204.0),
+    193: (129.0, 43.0, 144.0),
+    195: (252.0, 100.0, 106.0),
+    202: (187.0, 196.0, 73.0),
+    208: (13.0, 158.0, 40.0),
+    213: (52.0, 122.0, 152.0),
+    214: (128.0, 76.0, 202.0),
+    221: (187.0, 50.0, 115.0),
+    229: (180.0, 141.0, 71.0),
+    230: (77.0, 208.0, 35.0),
+    232: (72.0, 183.0, 168.0),
+    233: (97.0, 99.0, 203.0),
+    242: (172.0, 22.0, 158.0),
+    250: (155.0, 64.0, 40.0),
+    261: (118.0, 159.0, 30.0),
+    264: (69.0, 252.0, 148.0),
+    276: (45.0, 103.0, 173.0),
+    283: (111.0, 38.0, 149.0),
+    286: (184.0, 9.0, 49.0),
+    300: (188.0, 174.0, 67.0),
+    304: (53.0, 206.0, 53.0),
+    312: (97.0, 235.0, 252.0),
+    323: (66.0, 32.0, 182.0),
+    325: (236.0, 114.0, 195.0),
+    331: (241.0, 154.0, 83.0),
+    342: (133.0, 240.0, 52.0),
+    356: (16.0, 205.0, 144.0),
+    370: (75.0, 101.0, 198.0),
+    392: (237.0, 95.0, 251.0),
+    395: (191.0, 52.0, 49.0),
+    399: (227.0, 254.0, 54.0),
+    408: (49.0, 206.0, 87.0),
+    417: (48.0, 113.0, 150.0),
+    488: (125.0, 73.0, 182.0),
+    540: (229.0, 32.0, 114.0),
+    562: (158.0, 119.0, 28.0),
+    570: (60.0, 205.0, 27.0),
+    572: (18.0, 215.0, 201.0),
+    581: (79.0, 76.0, 153.0),
+    609: (134.0, 13.0, 116.0),
+    748: (192.0, 97.0, 63.0),
+    776: (108.0, 163.0, 18.0),
+    1156: (95.0, 220.0, 156.0),
+    1163: (98.0, 141.0, 208.0),
+    1164: (144.0, 19.0, 193.0),
+    1165: (166.0, 36.0, 57.0),
+    1166: (212.0, 202.0, 34.0),
+    1167: (23.0, 206.0, 34.0),
+    1168: (91.0, 211.0, 236.0),
+    1169: (79.0, 55.0, 137.0),
+    1170: (182.0, 19.0, 117.0),
+    1171: (134.0, 76.0, 14.0),
+    1172: (87.0, 185.0, 28.0),
+    1173: (82.0, 224.0, 187.0),
+    1174: (92.0, 110.0, 214.0),
+    1175: (168.0, 80.0, 171.0),
+    1176: (197.0, 63.0, 51.0),
+    1178: (175.0, 199.0, 77.0),
+    1179: (62.0, 180.0, 98.0),
+    1180: (8.0, 91.0, 150.0),
+    1181: (77.0, 15.0, 130.0),
+    1182: (154.0, 65.0, 96.0),
+    1183: (197.0, 152.0, 11.0),
+    1184: (59.0, 155.0, 45.0),
+    1185: (12.0, 147.0, 145.0),
+    1186: (54.0, 35.0, 219.0),
+    1187: (210.0, 73.0, 181.0),
+    1188: (221.0, 124.0, 77.0),
+    1189: (149.0, 214.0, 66.0),
+    1190: (72.0, 185.0, 134.0),
+    1191: (42.0, 94.0, 198.0),
+}
+
+# For instance segmentation the non-object categories
+VALID_PANOPTIC_IDS = (1, 3)
+
+CLASS_LABELS_PANOPTIC = ("wall", "floor")

code/pointcept/datasets/preprocessing/scannet/meta_data/scannet200_splits.py

@@ -0,0 +1,625 @@
+# This file contains the HEAD - COMMON - TAIL split category ids for ScanNet 200
+
+HEAD_CATS_SCANNET_200 = [
+    "tv stand",
+    "curtain",
+    "blinds",
+    "shower curtain",
+    "bookshelf",
+    "tv",
+    "kitchen cabinet",
+    "pillow",
+    "lamp",
+    "dresser",
+    "monitor",
+    "object",
+    "ceiling",
+    "board",
+    "stove",
+    "closet wall",
+    "couch",
+    "office chair",
+    "kitchen counter",
+    "shower",
+    "closet",
+    "doorframe",
+    "sofa chair",
+    "mailbox",
+    "nightstand",
+    "washing machine",
+    "picture",
+    "book",
+    "sink",
+    "recycling bin",
+    "table",
+    "backpack",
+    "shower wall",
+    "toilet",
+    "copier",
+    "counter",
+    "stool",
+    "refrigerator",
+    "window",
+    "file cabinet",
+    "chair",
+    "wall",
+    "plant",
+    "coffee table",
+    "stairs",
+    "armchair",
+    "cabinet",
+    "bathroom vanity",
+    "bathroom stall",
+    "mirror",
+    "blackboard",
+    "trash can",
+    "stair rail",
+    "box",
+    "towel",
+    "door",
+    "clothes",
+    "whiteboard",
+    "bed",
+    "floor",
+    "bathtub",
+    "desk",
+    "wardrobe",
+    "clothes dryer",
+    "radiator",
+    "shelf",
+]
+COMMON_CATS_SCANNET_200 = [
+    "cushion",
+    "end table",
+    "dining table",
+    "keyboard",
+    "bag",
+    "toilet paper",
+    "printer",
+    "blanket",
+    "microwave",
+    "shoe",
+    "computer tower",
+    "bottle",
+    "bin",
+    "ottoman",
+    "bench",
+    "basket",
+    "fan",
+    "laptop",
+    "person",
+    "paper towel dispenser",
+    "oven",
+    "rack",
+    "piano",
+    "suitcase",
+    "rail",
+    "container",
+    "telephone",
+    "stand",
+    "light",
+    "laundry basket",
+    "pipe",
+    "seat",
+    "column",
+    "bicycle",
+    "ladder",
+    "jacket",
+    "storage bin",
+    "coffee maker",
+    "dishwasher",
+    "machine",
+    "mat",
+    "windowsill",
+    "bulletin board",
+    "fireplace",
+    "mini fridge",
+    "water cooler",
+    "shower door",
+    "pillar",
+    "ledge",
+    "furniture",
+    "cart",
+    "decoration",
+    "closet door",
+    "vacuum cleaner",
+    "dish rack",
+    "range hood",
+    "projector screen",
+    "divider",
+    "bathroom counter",
+    "laundry hamper",
+    "bathroom stall door",
+    "ceiling light",
+    "trash bin",
+    "bathroom cabinet",
+    "structure",
+    "storage organizer",
+    "potted plant",
+    "mattress",
+]
+TAIL_CATS_SCANNET_200 = [
+    "paper",
+    "plate",
+    "soap dispenser",
+    "bucket",
+    "clock",
+    "guitar",
+    "toilet paper holder",
+    "speaker",
+    "cup",
+    "paper towel roll",
+    "bar",
+    "toaster",
+    "ironing board",
+    "soap dish",
+    "toilet paper dispenser",
+    "fire extinguisher",
+    "ball",
+    "hat",
+    "shower curtain rod",
+    "paper cutter",
+    "tray",
+    "toaster oven",
+    "mouse",
+    "toilet seat cover dispenser",
+    "storage container",
+    "scale",
+    "tissue box",
+    "light switch",
+    "crate",
+    "power outlet",
+    "sign",
+    "projector",
+    "candle",
+    "plunger",
+    "stuffed animal",
+    "headphones",
+    "broom",
+    "guitar case",
+    "dustpan",
+    "hair dryer",
+    "water bottle",
+    "handicap bar",
+    "purse",
+    "vent",
+    "shower floor",
+    "water pitcher",
+    "bowl",
+    "paper bag",
+    "alarm clock",
+    "music stand",
+    "laundry detergent",
+    "dumbbell",
+    "tube",
+    "cd case",
+    "closet rod",
+    "coffee kettle",
+    "shower head",
+    "keyboard piano",
+    "case of water bottles",
+    "coat rack",
+    "folded chair",
+    "fire alarm",
+    "power strip",
+    "calendar",
+    "poster",
+    "luggage",
+]
+
+
+# Given the different size of the official train and val sets, not all ScanNet200 categories are present in the validation set.
+# Here we list of categories with labels and IDs present in both train and validation set, and the remaining categories those are present in train, but not in val
+# We dont evaluate on unseen validation categories in this benchmark
+
+VALID_CLASS_IDS_200_VALIDATION = (
+    "wall",
+    "chair",
+    "floor",
+    "table",
+    "door",
+    "couch",
+    "cabinet",
+    "shelf",
+    "desk",
+    "office chair",
+    "bed",
+    "pillow",
+    "sink",
+    "picture",
+    "window",
+    "toilet",
+    "bookshelf",
+    "monitor",
+    "curtain",
+    "book",
+    "armchair",
+    "coffee table",
+    "box",
+    "refrigerator",
+    "lamp",
+    "kitchen cabinet",
+    "towel",
+    "clothes",
+    "tv",
+    "nightstand",
+    "counter",
+    "dresser",
+    "stool",
+    "cushion",
+    "plant",
+    "ceiling",
+    "bathtub",
+    "end table",
+    "dining table",
+    "keyboard",
+    "bag",
+    "backpack",
+    "toilet paper",
+    "printer",
+    "tv stand",
+    "whiteboard",
+    "blanket",
+    "shower curtain",
+    "trash can",
+    "closet",
+    "stairs",
+    "microwave",
+    "stove",
+    "shoe",
+    "computer tower",
+    "bottle",
+    "bin",
+    "ottoman",
+    "bench",
+    "board",
+    "washing machine",
+    "mirror",
+    "copier",
+    "basket",
+    "sofa chair",
+    "file cabinet",
+    "fan",
+    "laptop",
+    "shower",
+    "paper",
+    "person",
+    "paper towel dispenser",
+    "oven",
+    "blinds",
+    "rack",
+    "plate",
+    "blackboard",
+    "piano",
+    "suitcase",
+    "rail",
+    "radiator",
+    "recycling bin",
+    "container",
+    "wardrobe",
+    "soap dispenser",
+    "telephone",
+    "bucket",
+    "clock",
+    "stand",
+    "light",
+    "laundry basket",
+    "pipe",
+    "clothes dryer",
+    "guitar",
+    "toilet paper holder",
+    "seat",
+    "speaker",
+    "column",
+    "ladder",
+    "bathroom stall",
+    "shower wall",
+    "cup",
+    "jacket",
+    "storage bin",
+    "coffee maker",
+    "dishwasher",
+    "paper towel roll",
+    "machine",
+    "mat",
+    "windowsill",
+    "bar",
+    "toaster",
+    "bulletin board",
+    "ironing board",
+    "fireplace",
+    "soap dish",
+    "kitchen counter",
+    "doorframe",
+    "toilet paper dispenser",
+    "mini fridge",
+    "fire extinguisher",
+    "ball",
+    "hat",
+    "shower curtain rod",
+    "water cooler",
+    "paper cutter",
+    "tray",
+    "shower door",
+    "pillar",
+    "ledge",
+    "toaster oven",
+    "mouse",
+    "toilet seat cover dispenser",
+    "furniture",
+    "cart",
+    "scale",
+    "tissue box",
+    "light switch",
+    "crate",
+    "power outlet",
+    "decoration",
+    "sign",
+    "projector",
+    "closet door",
+    "vacuum cleaner",
+    "plunger",
+    "stuffed animal",
+    "headphones",
+    "dish rack",
+    "broom",
+    "range hood",
+    "dustpan",
+    "hair dryer",
+    "water bottle",
+    "handicap bar",
+    "vent",
+    "shower floor",
+    "water pitcher",
+    "mailbox",
+    "bowl",
+    "paper bag",
+    "projector screen",
+    "divider",
+    "laundry detergent",
+    "bathroom counter",
+    "object",
+    "bathroom vanity",
+    "closet wall",
+    "laundry hamper",
+    "bathroom stall door",
+    "ceiling light",
+    "trash bin",
+    "dumbbell",
+    "stair rail",
+    "tube",
+    "bathroom cabinet",
+    "closet rod",
+    "coffee kettle",
+    "shower head",
+    "keyboard piano",
+    "case of water bottles",
+    "coat rack",
+    "folded chair",
+    "fire alarm",
+    "power strip",
+    "calendar",
+    "poster",
+    "potted plant",
+    "mattress",
+)
+
+CLASS_LABELS_200_VALIDATION = (
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    21,
+    22,
+    23,
+    24,
+    26,
+    27,
+    28,
+    29,
+    31,
+    32,
+    33,
+    34,
+    35,
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+    39,
+    40,
+    41,
+    42,
+    44,
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+    48,
+    49,
+    50,
+    51,
+    52,
+    54,
+    55,
+    56,
+    57,
+    58,
+    59,
+    62,
+    63,
+    64,
+    65,
+    66,
+    67,
+    68,
+    69,
+    70,
+    71,
+    72,
+    73,
+    74,
+    75,
+    76,
+    77,
+    78,
+    79,
+    80,
+    82,
+    84,
+    86,
+    87,
+    88,
+    89,
+    90,
+    93,
+    95,
+    96,
+    97,
+    98,
+    99,
+    100,
+    101,
+    102,
+    103,
+    104,
+    105,
+    106,
+    107,
+    110,
+    112,
+    115,
+    116,
+    118,
+    120,
+    122,
+    125,
+    128,
+    130,
+    131,
+    132,
+    134,
+    136,
+    138,
+    139,
+    140,
+    141,
+    145,
+    148,
+    154,
+    155,
+    156,
+    157,
+    159,
+    161,
+    163,
+    165,
+    166,
+    168,
+    169,
+    170,
+    177,
+    180,
+    185,
+    188,
+    191,
+    193,
+    195,
+    202,
+    208,
+    213,
+    214,
+    229,
+    230,
+    232,
+    233,
+    242,
+    250,
+    261,
+    264,
+    276,
+    283,
+    300,
+    304,
+    312,
+    323,
+    325,
+    342,
+    356,
+    370,
+    392,
+    395,
+    408,
+    417,
+    488,
+    540,
+    562,
+    570,
+    609,
+    748,
+    776,
+    1156,
+    1163,
+    1164,
+    1165,
+    1166,
+    1167,
+    1168,
+    1169,
+    1170,
+    1171,
+    1172,
+    1173,
+    1175,
+    1176,
+    1179,
+    1180,
+    1181,
+    1182,
+    1184,
+    1185,
+    1186,
+    1187,
+    1188,
+    1189,
+    1191,
+)
+
+VALID_CLASS_IDS_200_TRAIN_ONLY = (
+    "bicycle",
+    "storage container",
+    "candle",
+    "guitar case",
+    "purse",
+    "alarm clock",
+    "music stand",
+    "cd case",
+    "structure",
+    "storage organizer",
+    "luggage",
+)
+
+CLASS_LABELS_200_TRAIN_ONLY = (
+    121,
+    221,
+    286,
+    331,
+    399,
+    572,
+    581,
+    1174,
+    1178,
+    1183,
+    1190,
+)

code/pointcept/datasets/preprocessing/scannet/meta_data/scannet_means.npz

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+oid sha256:df5c2bd40e8518e982c7d7b4b39020b07ac774695038bf49cb28b44e5760457e
+size 676

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+scene0630_05
+scene0630_06
+scene0706_00
+scene0567_00
+scene0567_01

code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels-old.combined.tsv

@@ -0,0 +1,608 @@
+id	raw_category	category	count	nyu40id	eigen13id	nyuClass	nyu40class	eigen13class	ModelNet40	ModelNet10	ShapeNetCore55	synsetoffset	wnsynsetid	wnsynsetkey	mpcat40	mpcat40index
+1	wall	wall	8277	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+2	chair	chair	4646	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+22	books	book	1678	23	2	book	books	Books					n02870526	book.n.11	objects	39
+3	floor	floor	1553	2	5	floor	floor	Floor					n03365592	floor.n.01	floor	2
+5	door	door	1483	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+1163	object	object	1313	40	7		otherprop	Objects							objects	39
+16	window	window	1209	9	13	window	window	Window					n04587648	window.n.01	window	9
+4	table	table	1170	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+56	trash can	trash can	1090	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+13	pillow	pillow	937	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+15	picture	picture	862	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+41	ceiling	ceiling	806	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+26	box	box	775	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+161	doorframe	doorframe	768	8	12	door	door	Wall	door					doorframe.n.01	door	4
+19	monitor	monitor	765	40	7	monitor	otherprop	Objects	monitor	monitor	tv or monitor	3211117	n03782190	monitor.n.04	objects	39
+7	cabinet	cabinet	731	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+9	desk	desk	680	14	10	desk	desk	Table	desk	desk	table	4379243	n03179701	desk.n.01	table	5
+8	shelf	shelf	641	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+10	office chair	office chair	595	5	4	chair	chair	Chair	chair	chair	chair	3001627	n04373704	swivel_chair.n.01	chair	3
+31	towel	towel	570	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+6	couch	couch	502	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+14	sink	sink	488	34	7	sink	sink	Objects	sink				n04223580	sink.n.01	sink	15
+48	backpack	backpack	479	40	7	backpack	otherprop	Objects					n02769748	backpack.n.01	objects	39
+28	lamp	lamp	419	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+11	bed	bed	370	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+18	bookshelf	bookshelf	360	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+71	mirror	mirror	349	19	7	mirror	mirror	Objects					n03773035	mirror.n.01	mirror	21
+21	curtain	curtain	347	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+40	plant	plant	331	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+52	whiteboard	whiteboard	327	30	7	whiteboard	whiteboard	Objects					n03211616	display_panel.n.01	board_panel	35
+96	radiator	radiator	322	39	6	radiator	otherfurniture	Furniture					n04041069	radiator.n.02	misc	40
+22	book	book	318	23	2	book	books	Books					n02870526	book.n.11	objects	39
+29	kitchen cabinet	kitchen cabinet	310	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+49	toilet paper	toilet paper	291	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+29	kitchen cabinets	kitchen cabinet	289	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+23	armchair	armchair	281	5	4	chair	chair	Chair	chair	chair	chair	3001627	n02738535	armchair.n.01	chair	3
+63	shoes	shoe	272	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+24	coffee table	coffee table	258	7	10	coffee table	table	Table	table	table	table	4379243	n03063968	coffee_table.n.01	table	5
+17	toilet	toilet	256	33	7	toilet	toilet	Objects	toilet	toilet			n04446276	toilet.n.01	toilet	18
+47	bag	bag	252	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+32	clothes	clothes	248	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+46	keyboard	keyboard	246	40	7	keyboard	otherprop	Objects	keyboard		computer keyboard	3085013	n03085013	computer_keyboard.n.01	objects	39
+65	bottle	bottle	226	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+97	recycling bin	recycling bin	225	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+34	nightstand	nightstand	224	32	6	night stand	night stand	Furniture	night_stand	night_stand			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+38	stool	stool	221	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+33	tv	tv	219	25	11	television	television	TV			tv or monitor	3211117	n03211117	display.n.06	tv_monitor	22
+75	file cabinet	file cabinet	217	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+36	dresser	dresser	213	17	6	dresser	dresser	Furniture	dresser	dresser			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+64	computer tower	computer tower	203	40	7	computer	otherprop	Objects					n03082979	computer.n.01	objects	39
+32	clothing	clothes	165	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+101	telephone	telephone	164	40	7	telephone	otherprop	Objects			telephone	4401088	n04401088	telephone.n.01	objects	39
+130	cup	cup	157	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+27	refrigerator	refrigerator	154	24	6	refridgerator	refridgerator	Furniture					n04070727	refrigerator.n.01	appliances	37
+44	end table	end table	147	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+131	jacket	jacket	146	40	7	jacket	otherprop	Objects					n03589791	jacket.n.01	clothes	38
+55	shower curtain	shower curtain	144	28	7	shower curtain	shower curtain	Objects	curtain				n04209239	shower_curtain.n.01	curtain	12
+42	bathtub	bathtub	144	36	7	bathtub	bathtub	Objects	bathtub	bathtub	tub	2808440	n02808440	bathtub.n.01	bathtub	25
+59	microwave	microwave	141	40	7	microwave	otherprop	Objects			microwave	3761084	n03761084	microwave.n.02	appliances	37
+159	kitchen counter	kitchen counter	140	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+74	sofa chair	sofa chair	129	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+82	paper towel dispenser	paper towel dispenser	129	40	7	paper towel dispenser	otherprop	Objects							objects	39
+1164	bathroom vanity	bathroom vanity	126	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	table	5
+93	suitcase	suitcase	118	40	7	luggage	otherprop	Objects					n02773838	bag.n.06	objects	39
+77	laptop	laptop	111	40	7	laptop	otherprop	Objects	laptop		laptop	3642806	n03642806	laptop.n.01	objects	39
+67	ottoman	ottoman	111	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+128	shower walls	shower wall	109	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+50	printer	printer	106	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+35	counter	counter	104	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+69	board	board	100	38	7	board	otherstructure	Objects							board_panel	35
+100	soap dispenser	soap dispenser	99	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+62	stove	stove	95	38	7	stove	otherstructure	Objects			stove	4330267	n04330267	stove.n.02	appliances	37
+105	light	light	93	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+1165	closet wall	closet wall	90	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+165	mini fridge	mini fridge	87	24	6	refridgerator	refridgerator	Furniture					n03273913	electric_refrigerator.n.01	appliances	37
+7	cabinets	cabinet	79	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+5	doors	door	76	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+76	fan	fan	75	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+230	tissue box	tissue box	73	40	7	tissue box	otherprop	Objects					n02883344	box.n.01	objects	39
+54	blanket	blanket	72	40	7	blanket	otherprop	Objects					n02849154	blanket.n.01	objects	39
+125	bathroom stall	bathroom stall	71	38	7		otherstructure	Objects					n02873839	booth.n.02	misc	40
+72	copier	copier	70	40	7		otherprop	Objects					n03257586	duplicator.n.01	appliances	37
+68	bench	bench	66	39	6	bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+145	bar	bar	66	38	7	bar	otherstructure	Objects					n02788689	bar.n.03	misc	40
+157	soap dish	soap dish	65	40	7	soap dish	otherprop	Objects					n04254009	soap_dish.n.01	objects	39
+1166	laundry hamper	laundry hamper	65	40	7	laundry basket	otherprop	Objects							objects	39
+132	storage bin	storage bin	63	40	7	storage bin	otherprop	Objects							objects	39
+1167	bathroom stall door	bathroom stall door	62	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+232	light switch	light switch	61	38	7	light switch	otherstructure	Objects					n04372370	switch.n.01	misc	40
+134	coffee maker	coffee maker	61	40	7		otherprop	Objects					n03063338	coffee_maker.n.01	appliances	37
+51	tv stand	tv stand	61	39	6	tv stand	otherfurniture	Furniture	tv_stand				n03290653	entertainment_center.n.01	furniture	36
+250	decoration	decoration	60	40	7		otherprop	Objects					n03169390	decoration.n.01	misc	40
+1168	ceiling light	ceiling light	59	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+342	range hood	range hood	59	38	7	range hood	otherstructure	Objects	range_hood				n04053677	range_hood.n.01	misc	40
+89	blackboard	blackboard	58	38	7	blackboard	otherstructure	Objects					n02846511	blackboard.n.01	board_panel	35
+103	clock	clock	58	40	7	clock	otherprop	Objects			clock	3046257	n03046257	clock.n.01	objects	39
+99	wardrobe closet	wardrobe	54	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+95	rail	rail	53	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+154	bulletin board	bulletin board	53	38	7	board	otherstructure	Objects					n03211616	display_panel.n.01	board_panel	35
+140	mat	mat	52	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1169	trash bin	trash bin	52	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+193	ledge	ledge	51	38	7		otherstructure	Objects					n09337253	ledge.n.01	misc	40
+116	seat	seat	49	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+202	mouse	mouse	49	40	7	mouse	otherprop	Objects					n03793489	mouse.n.04	objects	39
+73	basket	basket	48	40	7	basket	otherprop	Objects			basket	2801938	n02801938	basket.n.01	objects	39
+78	shower	shower	48	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+1170	dumbbell	dumbbell	48	40	7		otherprop	Objects					n03255030	dumbbell.n.01	objects	39
+79	paper	paper	46	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+80	person	person	46	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+141	windowsill	windowsill	45	38	7		otherstructure	Objects					n04590263	windowsill.n.01	window	9
+57	closet	closet	45	39	6	wardrobe	otherfurniture	Furniture	wardrobe						misc	40
+102	bucket	bucket	45	40	7	bucket	otherprop	Objects					n02909870	bucket.n.01	misc	40
+261	sign	sign	44	40	7	sign	otherprop	Objects					n04217882	signboard.n.01	objects	39
+118	speaker	speaker	43	40	7	speaker	otherprop	Objects			speaker	3691459	n03691459	loudspeaker.n.01	objects	39
+136	dishwasher	dishwasher	43	38	7	dishwasher	otherstructure	Objects			dishwasher	3207941	n03207941	dishwasher.n.01	appliances	37
+98	container	container	43	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1171	stair rail	stair rail	42	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+170	shower curtain rod	shower curtain rod	42	40	7		otherprop	Objects							curtain	12
+1172	tube	tube	41	40	7		otherprop	Objects							misc	40
+1173	bathroom cabinet	bathroom cabinet	39	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+79	papers	paper	39	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+221	storage container	storage container	39	40	7	container	otherprop	Objects							objects	39
+570	paper bag	paper bag	39	37	7	bag	bag	Objects					n04122825	sack.n.01	objects	39
+138	paper towel roll	paper towel roll	39	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+168	ball	ball	39	40	7	ball	otherprop	Objects							objects	39
+276	closet doors	closet door	38	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+106	laundry basket	laundry basket	37	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+214	cart	cart	37	40	7	cart	otherprop	Objects					n03484083	handcart.n.01	shelving	31
+276	closet door	closet door	35	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+323	dish rack	dish rack	35	40	7	dish rack	otherprop	Objects					n03207630	dish_rack.n.01	objects	39
+58	stairs	stairs	35	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+86	blinds	blinds	35	13	13	blinds	blinds	Window					n02851099	blind.n.03	blinds	32
+2	stack of chairs	chair	35	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+399	purse	purse	34	40	7	purse	otherprop	Objects					n02774152	bag.n.04	objects	39
+121	bicycle	bicycle	33	40	7	bicycle	otherprop	Objects			bicycle	2834778	n02834778	bicycle.n.01	objects	39
+185	tray	tray	32	40	7	tray	otherprop	Objects					n04476259	tray.n.01	objects	39
+300	plunger	plunger	30	40	7		otherprop	Objects					n03970156	plunger.n.03	objects	39
+180	paper cutter	paper cutter	30	40	7	paper cutter	otherprop	Objects					n03886940	paper_cutter.n.01	objects	39
+163	toilet paper dispenser	toilet paper dispenser	29	40	7		otherprop	Objects							objects	39
+26	boxes	box	29	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+66	bin	bin	28	40	7	bin	otherprop	Objects					n02839910	bin.n.01	objects	39
+208	toilet seat cover dispenser	toilet seat cover dispenser	28	40	7		otherprop	Objects							objects	39
+112	guitar	guitar	28	40	7	guitar	otherprop	Objects	guitar		guitar	3467517	n03467517	guitar.n.01	objects	39
+540	mailboxes	mailbox	28	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+395	handicap bar	handicap bar	27	38	7	bar	otherstructure	Objects							misc	40
+166	fire extinguisher	fire extinguisher	27	40	7	fire extinguisher	otherprop	Objects					n03345837	fire_extinguisher.n.01	misc	40
+122	ladder	ladder	27	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	stairs	16
+120	column	column	26	38	7	column	otherstructure	Objects					n03074380	column.n.06	column	24
+107	pipe	pipe	25	40	7	pipe	otherprop	Objects					n03944672	pipe.n.02	misc	40
+283	vacuum cleaner	vacuum cleaner	25	40	7		otherprop	Objects					n04517823	vacuum.n.04	objects	39
+88	plate	plate	24	40	7	plate	otherprop	Objects					n03959485	plate.n.04	objects	39
+90	piano	piano	24	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+177	water cooler	water cooler	24	39	6	water cooler	otherfurniture	Furniture					n04559166	water_cooler.n.01	misc	40
+1174	cd case	cd case	24	40	7		otherprop	Objects							objects	39
+562	bowl	bowl	24	40	7	bowl	otherprop	Objects	bowl		bowl	2880940	n02880940	bowl.n.03	objects	39
+1175	closet rod	closet rod	24	40	7		otherprop	Objects					n04100174	rod.n.01	misc	40
+1156	bathroom counter	bathroom counter	24	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+84	oven	oven	23	38	7	oven	otherstructure	Objects					n03862676	oven.n.01	appliances	37
+104	stand	stand	23	39	6	stand	otherfurniture	Furniture	table	table	table	4379243	n04301000	stand.n.04	table	5
+229	scale	scale	23	40	7	scale	otherprop	Objects					n04141975	scale.n.07	objects	39
+70	washing machine	washing machine	23	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+325	broom	broom	22	40	7	broom	otherprop	Objects					n02906734	broom.n.01	objects	39
+169	hat	hat	22	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+128	shower wall	shower wall	22	1	12	wall	wall	Wall					n04208936	shower.n.01	wall	1
+331	guitar case	guitar case	21	40	7	guitar case	otherprop	Objects							objects	39
+87	rack	rack	21	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+488	water pitcher	water pitcher	21	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+776	laundry detergent	laundry detergent	21	40	7		otherprop	Objects							objects	39
+370	hair dryer	hair dryer	21	40	7	hair dryer	otherprop	Objects					n03483316	hand_blower.n.01	objects	39
+191	pillar	pillar	21	38	7	column	otherstructure	Objects					n03073977	column.n.07	column	24
+748	divider	divider	20	40	7		otherprop	Objects							wall	1
+242	power outlet	power outlet	19	40	7		otherprop	Objects							misc	40
+45	dining table	dining table	19	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+417	shower floor	shower floor	19	2	5	floor	floor	Floor					n04208936	shower.n.01	floor	2
+70	washing machines	washing machine	19	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+188	shower door	shower door	19	8	12	door	door	Wall	door				n04208936	shower.n.01	door	4
+1176	coffee kettle	coffee kettle	18	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1177	wardrobe cabinet	wardrobe 	18	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1178	structure	structure	18	38	7		otherstructure	Objects							misc	40
+18	bookshelves	bookshelf	17	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+110	clothes dryer	clothes dryer	17	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+148	toaster	toaster	17	40	7	toaster	otherprop	Objects					n04442312	toaster.n.02	appliances	37
+63	shoe	shoe	17	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+155	ironing board	ironing board	16	39	6	ironing board	otherfurniture	Furniture					n03586090	ironing_board.n.01	objects	39
+572	alarm clock	alarm clock	16	40	7	alarm clock	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+1179	shower head	shower head	15	38	7		otherstructure	Objects							shower	23
+28	lamp base	lamp	15	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+392	water bottle	water bottle	15	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n04557648	water_bottle.n.01	objects	39
+1180	keyboard piano	keyboard piano	15	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+609	projector screen	projector screen	15	38	7	projector screen	otherstructure	Objects							misc	40
+1181	case of water bottles	case of water bottles	15	40	7		otherprop	Objects							objects	39
+195	toaster oven	toaster oven	14	40	7	toaster oven	otherprop	Objects					n04442441	toaster_oven.n.01	appliances	37
+581	music stand	music stand	14	39	6	music stand	otherfurniture	Furniture					n03801760	music_stand.n.01	furniture	36
+58	staircase	stairs	14	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+1182	coat rack	coat rack	14	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	3
+1183	storage organizer	storage organizer	14	40	7		otherprop	Objects							shelving	3
+139	machine	machine	14	40	7	machine	otherprop	Objects					n03699975	machine.n.01	appliances	37
+1184	folded chair	folded chair	14	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1185	fire alarm	fire alarm	14	40	7		otherprop	Objects					n03343737	fire_alarm.n.02	misc	40
+156	fireplace	fireplace	13	38	7	fireplace	otherstructure	Objects					n03346455	fireplace.n.01	fireplace	27
+408	vent	vent	13	40	7		otherprop	Objects					n04526241	vent.n.01	misc	40
+213	furniture	furniture	13	39	6	furniture	otherfurniture	Furniture					n03405725	furniture.n.01	furniture	36
+1186	power strip	power strip	13	40	7		otherprop	Objects							objects	39
+1187	calendar	calendar	13	40	7		otherprop	Objects							objects	39
+1188	poster	poster	13	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+115	toilet paper holder	toilet paper holder	13	40	7	toilet paper holder	otherprop	Objects							objects	39
+1189	potted plant	potted plant	12	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+304	stuffed animal	stuffed animal	12	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+1190	luggage	luggage	12	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+21	curtains	curtain	12	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+312	headphones	headphones	12	40	7		otherprop	Objects					n03261776	earphone.n.01	objects	39
+233	crate	crate	12	39	6	crate	otherfurniture	Furniture					n03127925	crate.n.01	objects	39
+286	candle	candle	12	40	7	candle	otherprop	Objects	lamp				n02948072	candle.n.01	objects	39
+264	projector	projector	12	40	7	projector	otherprop	Objects					n04009552	projector.n.02	objects	39
+110	clothes dryers	clothes dryer	12	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+1191	mattress	mattress	12	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+356	dustpan	dustpan	12	40	7		otherprop	Objects					n03259009	dustpan.n.02	objects	39
+25	drawer	drawer	11	39	6	drawer	otherfurniture	Furniture					n03233905	drawer.n.01	furniture	36
+750	rod	rod	11	40	7		otherprop	Objects			pistol	3948459	n03427202	gat.n.01	misc	40
+269	globe	globe	11	40	7	globe	otherprop	Objects							objects	39
+307	footrest	footrest	11	39	6	foot rest	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+410	piano bench	piano bench	11	39	6	piano bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+730	breakfast bar	breakfast bar	11	38	7	bar	otherstructure	Objects							counter	26
+216	step stool	step stool	11	40	7	step stool	otherprop	Objects	stool				n04315713	step_stool.n.01	stool	19
+1192	hand rail	hand rail	11	38	7	railing	otherstructure	Objects							railing	30
+119	vending machine	vending machine	11	40	7	machine	otherprop	Objects					n04525305	vending_machine.n.01	appliances	37
+682	ceiling fan	ceiling fan	11	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+434	swiffer	swiffer	11	40	7		otherprop	Objects							objects	39
+126	foosball table	foosball table	11	39	6	foosball table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+919	jar	jar	11	40	7	jar	otherprop	Objects			jar	3593526	n03593526	jar.n.01	objects	39
+85	footstool	footstool	11	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+1193	folded table	folded table	10	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+108	round table	round table	10	7	10	table	table	Table	table	table	table	4379243	n04114554	round_table.n.02	table	5
+135	hamper	hamper	10	40	7	basket	otherprop	Objects			basket	2801938	n03482405	hamper.n.02	objects	39
+1194	poster tube	poster tube	10	40	7		otherprop	Objects							objects	39
+432	case	case	10	40	7	case	otherprop	Objects							objects	39
+53	carpet	carpet	10	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+1195	thermostat	thermostat	10	40	7		otherprop	Objects					n04422875	thermostat.n.01	misc	40
+111	coat	coat	10	40	7	jacket	otherprop	Objects					n03057021	coat.n.01	clothes	38
+305	water fountain	water fountain	10	38	7	water fountain	otherstructure	Objects					n03241335	drinking_fountain.n.01	misc	40
+1125	smoke detector	smoke detector	10	40	7		otherprop	Objects							misc	40
+13	pillows	pillow	9	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+1196	flip flops	flip flops	9	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+1197	cloth	cloth	9	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1198	banner	banner	9	40	7		otherprop	Objects					n02788021	banner.n.01	misc	40
+1199	clothes hanger	clothes hanger	9	40	7		otherprop	Objects					n03057920	coat_hanger.n.01	objects	39
+1200	whiteboard eraser	whiteboard eraser	9	40	7		otherprop	Objects							objects	39
+378	iron	iron	9	40	7		otherprop	Objects					n03584829	iron.n.04	objects	39
+591	instrument case	instrument case	9	40	7	case	otherprop	Objects							objects	39
+49	toilet paper rolls	toilet paper	9	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+92	soap	soap	9	40	7	soap	otherprop	Objects					n04253437	soap.n.01	objects	39
+1098	block	block	9	40	7		otherprop	Objects							misc	40
+291	wall hanging	wall hanging	8	40	7		otherprop	Objects					n03491178	hanging.n.01	picture	6
+1063	kitchen island	kitchen island	8	38	7	kitchen island	otherstructure	Objects					n03620600	kitchen_island.n.01	counter	26
+107	pipes	pipe	8	38	7		otherstructure	Objects							misc	40
+1135	toothbrush	toothbrush	8	40	7	toothbrush	otherprop	Objects					n04453156	toothbrush.n.01	objects	39
+189	shirt	shirt	8	40	7		otherprop	Objects					n04197391	shirt.n.01	clothes	38
+245	cutting board	cutting board	8	40	7	cutting board	otherprop	Objects					n03025513	chopping_board.n.01	objects	39
+194	vase	vase	8	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1201	shower control valve	shower control valve	8	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+386	exercise machine	exercise machine	8	40	7	machine	otherprop	Objects							gym_equipment	33
+1202	compost bin	compost bin	8	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+857	shorts	shorts	8	40	7	shorts	otherprop	Objects							clothes	38
+452	tire	tire	8	40	7		otherprop	Objects					n04440749	tire.n.01	objects	39
+1203	teddy bear	teddy bear	7	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+346	bathrobe	bathrobe	7	40	7		otherprop	Objects					n02807616	bathrobe.n.01	clothes	38
+152	handrail	handrail	7	38	7	railing	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+83	faucet	faucet	7	40	7	faucet	otherprop	Objects			faucet	3325088	n03325088	faucet.n.01	misc	40
+1204	pantry wall	pantry wall	7	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+726	thermos	thermos	7	40	7	flask	otherprop	Objects	bottle		bottle	2876657	n04422727	thermos.n.01	objects	39
+61	rug	rug	7	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+39	couch cushions	cushion	7	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1117	tripod	tripod	7	39	6	stand	otherfurniture	Furniture					n04485082	tripod.n.01	objects	39
+540	mailbox	mailbox	7	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+1205	tupperware	tupperware	7	40	7		otherprop	Objects							objects	39
+415	shoe rack	shoe rack	7	40	7	shoe rack	otherprop	Objects							shelving	31
+31	towels	towel	6	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+1206	beer bottles	beer bottle	6	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+153	treadmill	treadmill	6	39	6	treadmill	otherfurniture	Furniture					n04477387	treadmill.n.01	gym_equipment	33
+1207	salt	salt	6	40	7		otherprop	Objects							objects	39
+129	chest	chest	6	39	6	chest	otherfurniture	Furniture	dresser	dresser					chest_of_drawers	13
+220	dispenser	dispenser	6	40	7		otherprop	Objects					n03210683	dispenser.n.01	objects	39
+1208	mirror doors	mirror door	6	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+231	remote	remote	6	40	7		otherprop	Objects			remote_control	4074963	n04074963	remote_control.n.01	objects	39
+1209	folded ladder	folded ladder	6	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	misc	40
+39	cushion	cushion	6	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1210	carton	carton	6	40	7		otherprop	Objects							objects	39
+117	step	step	6	38	7		otherstructure	Objects					n04314914	step.n.04	misc	40
+822	drying rack	drying rack	6	39	6	drying rack	otherfurniture	Furniture							shelving	31
+238	slippers	slipper	6	40	7	shoe	otherprop	Objects					n04241394	slipper.n.01	clothes	38
+143	pool table	pool table	6	39	6	pool table	otherfurniture	Furniture	table	table	table	4379243	n03982430	pool_table.n.01	table	5
+1211	soda stream	soda stream	6	40	7		otherprop	Objects							objects	39
+228	toilet brush	toilet brush	6	40	7	toilet brush	otherprop	Objects							objects	39
+494	loft bed	loft bed	6	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+226	cooking pot	cooking pot	6	40	7	pot	otherprop	Objects							objects	39
+91	heater	heater	6	39	6	heater	otherfurniture	Furniture					n03508101	heater.n.01	misc	40
+1072	messenger bag	messenger bag	6	37	7	bag	bag	Objects							objects	39
+435	stapler	stapler	6	40	7	stapler	otherprop	Objects					n04303497	stapler.n.01	objects	39
+1165	closet walls	closet wall	5	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+345	scanner	scanner	5	40	7		otherprop	Objects							appliances	37
+893	elliptical machine	elliptical machine	5	40	7	machine	otherprop	Objects							gym_equipment	33
+621	kettle	kettle	5	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1212	metronome	metronome	5	40	7		otherprop	Objects					n03757604	metronome.n.01	objects	39
+297	dumbell	dumbell	5	40	7		otherprop	Objects							objects	39
+1213	music book	music book	5	23	2	book	books	Books					n02870526	book.n.11	objects	39
+1214	rice cooker	rice cooker	5	40	7		otherprop	Objects							objects	39
+1215	dart board	dart board	5	38	7	board	otherstructure	Objects					n03162940	dartboard.n.01	objects	39
+529	sewing machine	sewing machine	5	40	7	sewing machine	otherprop	Objects					n04179913	sewing_machine.n.01	objects	39
+1216	grab bar	grab bar	5	38	7	railing	otherstructure	Objects							railing	30
+1217	flowerpot	flowerpot	5	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1218	painting	painting	5	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1219	railing	railing	5	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+1220	stair	stair	5	38	7	stairs	otherstructure	Objects	stairs				n04314914	step.n.04	stairs	16
+525	toolbox	toolbox	5	39	6	chest	otherfurniture	Furniture					n04452615	toolbox.n.01	objects	39
+204	nerf gun	nerf gun	5	40	7		otherprop	Objects							objects	39
+693	binders	binder	5	40	7	binder	otherprop	Objects							objects	39
+179	desk lamp	desk lamp	5	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1221	quadcopter	quadcopter	5	40	7		otherprop	Objects							objects	39
+1222	pitcher	pitcher	5	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+1223	hanging	hanging	5	40	7		otherprop	Objects							misc	40
+1224	mail	mail	5	40	7		otherprop	Objects							misc	40
+1225	closet ceiling	closet ceiling	5	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+1226	hoverboard	hoverboard	5	40	7		otherprop	Objects							objects	39
+1227	beanbag chair	beanbag chair	5	39	6	bean bag	otherfurniture	Furniture					n02816656	beanbag.n.01	chair	3
+571	water heater	water heater	5	40	7	water heater	otherprop	Objects					n04560113	water_heater.n.01	misc	40
+1228	spray bottle	spray bottle	5	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+556	rope	rope	5	40	7	rope	otherprop	Objects					n04108268	rope.n.01	objects	39
+280	plastic container	plastic container	5	40	7	container	otherprop	Objects							objects	39
+1229	soap bottle	soap bottle	5	40	7	soap	otherprop	Objects							objects	39
+1230	ikea bag	ikea bag	4	37	7	bag	bag	Objects				2773838	n02773838	bag.n.06	objects	39
+1231	sleeping bag	sleeping bag	4	40	7		otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+1232	duffel bag	duffel bag	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+746	frying pan	frying pan	4	40	7	frying pan	otherprop	Objects					n03400231	frying_pan.n.01	objects	39
+1233	oven mitt	oven mitt	4	40	7		otherprop	Objects							objects	39
+1234	pot	pot	4	40	7	pot	otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+144	hand dryer	hand dryer	4	40	7		otherprop	Objects							objects	39
+282	dollhouse	dollhouse	4	39	6	doll house	otherfurniture	Furniture					n03219483	dollhouse.n.01	objects	39
+167	shampoo bottle	shampoo bottle	4	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1235	hair brush	hair brush	4	40	7		otherprop	Objects					n02908217	brush.n.02	objects	39
+1236	tennis racket	tennis racket	4	40	7		otherprop	Objects					n04409806	tennis_racket.n.01	objects	39
+1237	display case	display case	4	40	7	case	otherprop	Objects							objects	39
+234	ping pong table	ping pong table	4	39	6	ping pong table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+563	boiler	boiler	4	40	7		otherprop	Objects							misc	40
+1238	bag of coffee beans	bag of coffee beans	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+1239	bananas	banana	4	40	7		otherprop	Objects					n00021265	food.n.01	objects	39
+1240	carseat	carseat	4	40	7		otherprop	Objects							misc	40
+366	helmet	helmet	4	40	7		otherprop	Objects			helmet	3513137	n03513137	helmet.n.02	clothes	38
+816	umbrella	umbrella	4	40	7	umbrella	otherprop	Objects					n04507155	umbrella.n.01	objects	39
+1241	coffee box	coffee box	4	40	7		otherprop	Objects							objects	39
+719	envelope	envelope	4	40	7	envelope	otherprop	Objects					n03291819	envelope.n.01	objects	39
+284	wet floor sign	wet floor sign	4	40	7	sign	otherprop	Objects							misc	40
+1242	clothing rack	clothing rack	4	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+247	controller	controller	4	40	7		otherprop	Objects					n03096960	control.n.09	objects	39
+1243	bath walls	bathroom wall	4	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+1244	podium	podium	4	39	6		otherfurniture	Furniture					n03159640	dais.n.01	furniture	36
+1245	storage box	storage box	4	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1246	dolly	dolly	4	40	7		otherprop	Objects							misc	40
+1247	shampoo	shampoo	3	40	7		otherprop	Objects					n04183516	shampoo.n.01	objects	39
+592	paper tray	paper tray	3	40	7	paper tray	otherprop	Objects							objects	39
+385	cabinet door	cabinet door	3	8	12	door	door	Wall	door						door	4
+1248	changing station	changing station	3	40	7		otherprop	Objects							misc	40
+1249	poster printer	poster printer	3	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+133	screen	screen	3	40	7		otherprop	Objects					n03151077	curtain.n.01	curtain	12
+301	soap bar	soap bar	3	38	7	bar	otherstructure	Objects							objects	39
+1250	crutches	crutches	3	40	7		otherprop	Objects					n03141823	crutch.n.01	objects	39
+379	studio light	studio light	3	38	7	light	otherstructure	Objects							lighting	28
+130	stack of cups	cup	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+1251	toilet flush button	toilet flush button	3	40	7		otherprop	Objects							objects	39
+450	trunk	trunk	3	40	7		otherprop	Objects							misc	40
+1252	grocery bag	grocery bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03461288	grocery_bag.n.01	objects	39
+316	plastic bin	plastic bin	3	40	7	bin	otherprop	Objects							objects	39
+1253	pizza box	pizza box	3	29	7	box	box	Objects							objects	39
+385	cabinet doors	cabinet door	3	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	door	4
+1254	legs	legs	3	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+461	car	car	3	40	7	car	otherprop	Objects	car		car	2958343	n02958343	car.n.01	misc	40
+1255	shaving cream	shaving cream	3	40	7		otherprop	Objects					n04186051	shaving_cream.n.01	objects	39
+1256	luggage stand	luggage stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+599	shredder	shredder	3	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+281	statue	statue	3	40	7	sculpture	otherprop	Objects					n04306847	statue.n.01	misc	40
+1257	urinal	urinal	3	33	7	toilet	toilet	Objects	toilet	toilet			n04515991	urinal.n.01	toilet	18
+1258	hose	hose	3	40	7		otherprop	Objects					n03539875	hose.n.03	misc	40
+1259	bike pump	bike pump	3	40	7		otherprop	Objects							objects	39
+319	coatrack	coatrack	3	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1260	bear	bear	3	40	7		otherprop	Objects							objects	39
+28	wall lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1261	humidifier	humidifier	3	40	7		otherprop	Objects							objects	39
+546	toothpaste	toothpaste	3	40	7	toothpaste	otherprop	Objects							objects	39
+1262	mouthwash bottle	mouthwash bottle	3	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1263	poster cutter	poster cutter	3	40	7		otherprop	Objects							objects	39
+1264	golf bag	golf bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03445617	golf_bag.n.01	objects	39
+1265	food container	food container	3	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1266	camera	camera	3	40	7		otherprop	Objects							objects	39
+28	table lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n04380533	table_lamp.n.01	lighting	28
+1267	yoga mat	yoga mat	3	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1268	card	card	3	40	7		otherprop	Objects							objects	39
+1269	mug	mug	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+188	shower doors	shower door	3	38	7		otherstructure	Objects					n04208936	shower.n.01	door	4
+689	cardboard	cardboard	3	40	7		otherprop	Objects							objects	39
+1270	rack stand	rack stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+1271	boxes of paper	boxes of paper	3	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1272	flag	flag	3	40	7		otherprop	Objects							misc	40
+354	futon	futon	3	39	6	mattress	otherfurniture	Furniture					n03408444	futon.n.01	sofa	10
+339	magazine	magazine	3	40	7	magazine	otherprop	Objects					n06595351	magazine.n.01	objects	39
+1009	exit sign	exit sign	3	40	7	exit sign	otherprop	Objects							misc	40
+1273	rolled poster	rolled poster	3	40	7		otherprop	Objects							objects	39
+1274	wheel	wheel	3	40	7		otherprop	Objects							objects	39
+15	pictures	picture	3	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1275	blackboard eraser	blackboard eraser	3	40	7	eraser	otherprop	Objects					n03294833	eraser.n.01	objects	39
+361	organizer	organizer	3	40	7		otherprop	Objects					n03918737	personal_digital_assistant.n.01	objects	39
+1276	doll	doll	3	40	7	toy	otherprop	Objects					n03219135	doll.n.01	objects	39
+326	book rack	book rack	3	39	6	bookrack	otherfurniture	Furniture							objects	39
+1277	laundry bag	laundry bag	3	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+1278	sponge	sponge	3	40	7		otherprop	Objects					n01906749	sponge.n.04	objects	39
+116	seating	seat	3	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+1184	folded chairs	folded chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1279	lotion bottle	lotion bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+212	can	can	2	40	7	can	otherprop	Objects			can	2946921	n02946921	can.n.01	objects	39
+1280	lunch box	lunch box	2	40	7		otherprop	Objects							objects	39
+1281	food display	food display	2	40	7		otherprop	Objects							misc	40
+794	storage shelf	storage shelf	2	40	7		otherprop	Objects							shelving	31
+1282	sliding wood door	sliding wood door	2	40	7		otherprop	Objects							door	4
+955	pants	pants	2	40	7		otherprop	Objects					n04489008	trouser.n.01	clothes	38
+387	wood	wood	2	40	7		otherprop	Objects							misc	40
+69	boards	board	2	38	7	board	otherstructure	Objects							board_panel	35
+65	bottles	bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+523	washcloth	washcloth	2	40	7		otherprop	Objects					n04554523	washcloth.n.01	towel	20
+389	workbench	workbench	2	39	6	bench	otherfurniture	Furniture	bench		table	4379243	n04600486	workbench.n.01	table	5
+29	open kitchen cabinet	kitchen cabinet	2	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+1283	organizer shelf	organizer shelf	2	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+146	frame	frame	2	38	7		otherstructure	Objects							misc	40
+130	cups	cup	2	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+372	exercise ball	exercise ball	2	40	7	ball	otherprop	Objects					n04285146	sports_equipment.n.01	gym_equipment	33
+289	easel	easel	2	39	6	stand	otherfurniture	Furniture					n03262809	easel.n.01	furniture	36
+440	garbage bag	garbage bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+321	roomba	roomba	2	40	7		otherprop	Objects							objects	39
+976	garage door	garage door	2	38	7	garage door	otherstructure	Objects	door						door	4
+1256	luggage rack	luggage stand	2	39	6	stand	otherfurniture	Furniture					n04038440		shelving	31
+1284	bike lock	bike lock	2	40	7		otherprop	Objects							objects	39
+1285	briefcase	briefcase	2	40	7		otherprop	Objects					n02900705	briefcase.n.01	objects	39
+357	hand towel	hand towel	2	27	7	towel	towel	Objects					n03490006	hand_towel.n.01	towel	20
+1286	bath products	bath product	2	40	7		otherprop	Objects							objects	39
+1287	star	star	2	40	7		otherprop	Objects					n09444783	star.n.03	misc	40
+365	map	map	2	40	7	map	otherprop	Objects					n03720163	map.n.01	misc	40
+1288	coffee bean bag	coffee bean bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+81	headboard	headboard	2	39	6	headboard	otherfurniture	Furniture					n03502200	headboard.n.01	bed	11
+1289	ipad	ipad	2	40	7		otherprop	Objects							objects	39
+1290	display rack	display rack	2	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+948	traffic cone	traffic cone	2	40	7	cone	otherprop	Objects	cone						objects	39
+174	toiletry	toiletry	2	40	7		otherprop	Objects					n04447443	toiletry.n.01	objects	39
+1028	canopy	canopy	2	40	7		otherprop	Objects							misc	40
+1291	massage chair	massage chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1292	paper organizer	paper organizer	2	40	7		otherprop	Objects							objects	39
+1005	barricade	barricade	2	40	7		otherprop	Objects							misc	40
+235	platform	platform	2	38	7		otherstructure	Objects							misc	40
+1293	cap	cap	2	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+1294	dumbbell plates	dumbbell plates	2	40	7		otherprop	Objects							objects	39
+1295	elevator	elevator	2	38	7		otherstructure	Objects							misc	40
+1296	cooking pan	cooking pan	2	40	7	pan	otherprop	Objects					n03880531	pan.n.01	objects	39
+1297	trash bag	trash bag	2	37	7	bag	bag	Objects							objects	39
+1298	santa	santa	2	40	7		otherprop	Objects							misc	40
+1299	jewelry box	jewelry box	2	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1300	boat	boat	2	40	7		otherprop	Objects							misc	40
+1301	sock	sock	2	21	7	clothes	clothes	Objects					n04254777	sock.n.01	clothes	38
+1051	kinect	kinect	2	40	7	kinect	otherprop	Objects							objects	39
+566	crib	crib	2	39	6	crib	otherfurniture	Furniture							furniture	36
+1302	plastic storage bin	plastic storage bin	2	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1062	cooler	cooler	2	24	6	refridgerator	refridgerator	Furniture					n03102654	cooler.n.01	appliances	37
+1303	kitchen apron	kitchen apron	2	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1304	dishwashing soap bottle	dishwashing soap bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1305	xbox controller	xbox controller	2	40	7		otherprop	Objects							objects	39
+1306	banana holder	banana holder	2	40	7		otherprop	Objects							objects	39
+298	ping pong paddle	ping pong paddle	2	40	7		otherprop	Objects							table	5
+1307	airplane	airplane	2	40	7		otherprop	Objects							misc	40
+1308	conditioner bottle	conditioner bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1309	tea kettle	tea kettle	2	40	7	tea kettle	otherprop	Objects					n04397768	teakettle.n.01	objects	39
+43	bedframe	bedframe	2	39	6		otherfurniture	Furniture					n02822579	bedstead.n.01	bed	11
+1310	wood beam	wood beam	2	38	7		otherstructure	Objects							beam	29
+593	toilet paper package	toilet paper package	2	40	7		otherprop	Objects							objects	39
+1311	wall mounted coat rack	wall mounted coat rack	2	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1312	film light	film light	2	40	7		otherprop	Objects							lighting	28
+749	ceiling lamp	ceiling lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+623	chain	chain	1	40	7		otherprop	Objects							chair	3
+1313	sofa	sofa	1	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+99	closet wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+265	sweater	sweater	1	40	7		otherprop	Objects					n04370048	sweater.n.01	clothes	38
+1314	kitchen mixer	kitchen mixer	1	40	7		otherprop	Objects							appliances	37
+99	wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1315	water softener	water softener	1	40	7		otherprop	Objects							misc	40
+448	banister	banister	1	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+257	trolley	trolley	1	40	7	trolley	otherprop	Objects					n04335435	streetcar.n.01	misc	40
+1316	pantry shelf	pantry shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+786	sofa bed	sofa bed	1	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+801	loofa	loofa	1	40	7		otherprop	Objects							objects	39
+972	shower faucet handle	shower faucet handle	1	40	7	handle	otherprop	Objects							shower	23
+1317	toy piano	toy piano	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1318	fish	fish	1	40	7		otherprop	Objects					n02512053	fish.n.01	objects	39
+75	file cabinets	file cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n03337140	file.n.03	cabinet	7
+657	cat litter box	cat litter box	1	29	7	box	box	Objects							objects	39
+561	electric panel	electric panel	1	40	7		otherprop	Objects							misc	40
+93	suitcases	suitcase	1	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+513	curtain rod	curtain rod	1	38	7	curtain rod	otherstructure	Objects							curtain	12
+411	bunk bed	bunk bed	1	39	6	bunk bed	otherfurniture	Furniture	bed	bed	bed	2818832	n02920259	bunk_bed.n.01	bed	11
+1122	chandelier	chandelier	1	38	7	chandelier	otherstructure	Objects					n03005285	chandelier.n.01	lighting	28
+922	tape	tape	1	40	7	tape	otherprop	Objects							objects	39
+88	plates	plate	1	40	7		otherprop	Objects					n03959485	plate.n.04	objects	39
+518	alarm	alarm	1	40	7	alarm	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+814	fire hose	fire hose	1	40	7		otherprop	Objects					n03346004	fire_hose.n.01	misc	40
+1319	toy dinosaur	toy dinosaur	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1320	cone	cone	1	40	7		otherprop	Objects							objects	39
+649	glass doors	glass door	1	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+607	hatrack	hatrack	1	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+819	subwoofer	subwoofer	1	40	7	speaker	otherprop	Objects			speaker	3691459	n04349401	subwoofer.n.01	objects	39
+1321	fire sprinkler	fire sprinkler	1	40	7		otherprop	Objects							misc	40
+1322	trash cabinet	trash cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+1204	pantry walls	pantry wall	1	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+227	photo	photo	1	40	7	photo	otherprop	Objects					n03925226	photograph.n.01	picture	6
+817	barrier	barrier	1	40	7		otherprop	Objects					n02796623	barrier.n.01	misc	40
+130	stacks of cups	cup	1	40	7		otherprop	Objects					n03147509	cup.n.01	objects	39
+712	beachball	beachball	1	40	7	ball	otherprop	Objects					n02814224	beach_ball.n.01	objects	39
+1323	folded boxes	folded boxes	1	40	7		otherprop	Objects							objects	39
+1324	contact lens solution bottle	contact lens solution bottle	1	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+673	covered box	covered box	1	29	7	box	box	Objects							objects	39
+459	folder	folder	1	40	7	folder	otherprop	Objects					n03376279	folder.n.02	objects	39
+643	mail trays	mail tray	1	40	7	mail tray	otherprop	Objects							objects	39
+238	slipper	slipper	1	40	7		otherprop	Objects					n04241394	slipper.n.01	clothes	38
+765	magazine rack	magazine rack	1	39	6	stand	otherfurniture	Furniture					n03704549	magazine_rack.n.01	shelving	31
+1008	sticker	sticker	1	40	7	sticker	otherprop	Objects					n07272545	gummed_label.n.01	objects	39
+225	lotion	lotion	1	40	7		otherprop	Objects					n03690938	lotion.n.01	objects	39
+1083	buddha	buddha	1	40	7		otherprop	Objects							objects	39
+813	file organizer	file organizer	1	40	7		otherprop	Objects							objects	39
+138	paper towel rolls	paper towel roll	1	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+1145	night lamp	night lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+796	fuse box	fuse box	1	40	7		otherprop	Objects							misc	40
+1325	knife block	knife block	1	40	7		otherprop	Objects							objects	39
+363	furnace	furnace	1	39	6	furnace	otherfurniture	Furniture					n03404449	furnace.n.01		
+1174	cd cases	cd case	1	40	7		otherprop	Objects							objects	39
+38	stools	stool	1	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+1326	hand sanitzer dispenser	hand sanitzer dispenser	1	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+997	teapot	teapot	1	40	7	tea pot	otherprop	Objects					n04398044	teapot.n.01	objects	39
+1327	pen holder	pen holder	1	40	7		otherprop	Objects							objects	39
+1328	tray rack	tray rack	1	40	7		otherprop	Objects							objects	39
+1329	wig	wig	1	40	7		otherprop	Objects					n04584207	wig.n.01	objects	39
+182	switch	switch	1	40	7		otherprop	Objects					n04372370	switch.n.01	misc	40
+280	plastic containers	plastic container	1	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1330	night light	night light	1	40	7		otherprop	Objects							lighting	28
+1331	notepad	notepad	1	40	7		otherprop	Objects							objects	39
+1332	mail bin	mail bin	1	40	7		otherprop	Objects							misc	40
+1333	elevator button	elevator button	1	40	7		otherprop	Objects							misc	40
+939	gaming wheel	gaming wheel	1	40	7		otherprop	Objects							objects	39
+1334	drum set	drum set	1	40	7		otherprop	Objects							objects	39
+480	cosmetic bag	cosmetic bag	1	37	7	bag	bag	Objects							objects	39
+907	coffee mug	coffee mug	1	40	7	vessel	otherprop	Objects			cup or mug	3797390	n03063599	coffee_mug.n.01	objects	39
+1335	closet shelf	closet shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+1336	baby mobile	baby mobile	1	40	7		otherprop	Objects							objects	39
+829	diaper bin	diaper bin	1	40	7	bin	otherprop	Objects							objects	39
+947	door wall	door wall	1	1	12	wall	wall	Wall							wall	1
+1116	stepstool	stepstool	1	40	7	step stool	otherprop	Objects							objects	39
+599	paper shredder	shredder	1	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+733	dress rack	dress rack	1	40	7		otherprop	Objects					n03238762	dress_rack.n.01	misc	40
+123	cover	cover	1	40	7	blanket	otherprop	Objects							objects	39
+506	shopping bag	shopping bag	1	37	7	bag	bag	Objects					n04204081	shopping_bag.n.01	objects	39
+569	sliding door	sliding door	1	8	12	door	door	Wall	door				n04239074	sliding_door.n.01	door	4
+1337	exercise bike	exercise bike	1	40	7	machine	otherprop	Objects					n04210120	shredder.n.01	gym_equipment	33
+1338	recliner chair	recliner chair	1	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03238762	dress_rack.n.01	chair	3
+1314	kitchenaid mixer	kitchen mixer	1	40	7		otherprop	Objects							appliances	37
+1339	soda can	soda can	1	40	7	can	otherprop	Objects			can	2946921	n02946921	can.n.01	objects	39
+1340	stovetop	stovetop	1	38	7	stove	otherstructure	Objects			stove	4330267	n04330267	stove.n.02	appliances	37
+851	stepladder	stepladder	1	39	6	ladder	otherfurniture	Furniture	stairs				n04315599	step_ladder.n.01	stairs	16
+142	tap	tap	1	40	7	faucet	otherprop	Objects			faucet	3325088	n04559451	water_faucet.n.01	objects	39
+436	cable	cable	1	40	7	cables	otherprop	Objects							objects	39
+1341	baby changing station	baby changing station	1	39	6		otherfurniture	Furniture							furniture	36
+1342	costume	costume	1	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+885	rocking chair	rocking chair	1	5	4	chair	chair	Chair	chair	chair	chair	3001627	n04099969	rocking_chair.n.01	chair	3
+693	binder	binder	1	40	7	binder	otherprop	Objects							objects	39
+815	media center	media center	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+401	towel rack	towel rack	1	40	7		otherprop	Objects					n04459773	towel_rack.n.01	misc	40
+1343	medal	medal	1	40	7		otherprop	Objects							objects	39
+1184	stack of folded chairs	folded chair	1	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1344	telescope	telescope	1	40	7		otherprop	Objects					n04403638	telescope.n.01	objects	39
+1345	closet doorframe	closet doorframe	1	8	12	door	door	Wall	door						door	4
+160	glass	glass	1	38	7	glass	otherstructure	Objects					n03438257	glass.n.02	misc	40
+1126	baseball cap	baseball cap	1	40	7		otherprop	Objects			cap	2954340	n02799323	baseball_cap.n.01	clothes	38
+1346	battery disposal jar	battery disposal jar	1	40	7	jar	otherprop	Objects			jar	3593526	n03593526	jar.n.01	objects	39
+332	mop	mop	1	40	7		otherprop	Objects					n04367480	swab.n.02	objects	39
+397	tank	tank	1	40	7		otherprop	Objects							objects	39
+643	mail tray	mail tray	1	40	7	mail tray	otherprop	Objects							objects	39
+551	centerpiece	centerpiece	1	40	7	centerpiece	otherprop	Objects					n02994419	centerpiece.n.02	objects	39
+1163	stick	stick	1	40	7	stick	otherprop	Objects							objects	39
+1347	closet floor	closet floor	1	2	5	floor	floor	Floor					n03365592	floor.n.01	floor	2
+1348	dryer sheets	dryer sheets	1	40	7		otherprop	Objects							objects	39
+803	bycicle	bycicle	1	40	7		otherprop	Objects							misc	40
+484	flower stand	flower stand	1	39	6	stand	otherfurniture	Furniture							furniture	36
+1349	air mattress	air mattress	1	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02690809	air_mattress.n.01	bed	11
+1350	clip	clip	1	40	7		otherprop	Objects							objects	39
+222	side table	side table	1	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+1253	pizza boxes	pizza box	1	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1351	display	display	1	39	7		otherfurniture	Furniture					n03211117	display.n.06	misc	40
+1352	postcard	postcard	1	40	7		otherprop	Objects							objects	39
+828	display sign	display sign	1	40	7	sign	otherprop	Objects							misc	40
+1353	paper towel	paper towel	1	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+612	boots	boot	1	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+1354	tennis racket bag	tennis racket bag	1	40	7		otherprop	Objects							objects	39
+1355	air hockey table	air hockey table	1	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+1301	socks	sock	1	21	7	clothes	clothes	Objects					n04254777	sock.n.01	clothes	38
+1356	food bag	food bag	1	37	7	bag	bag	Objects							objects	39
+1199	clothes hangers	clothes hanger	1	40	7		otherprop	Objects					n03057920	coat_hanger.n.01	misc	40
+1357	starbucks cup	starbucks cup	1	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39

code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv

@@ -0,0 +1,608 @@
+id	raw_category	category	count	nyu40id	eigen13id	nyuClass	nyu40class	eigen13class	ModelNet40	ModelNet10	ShapeNetCore55	synsetoffset	wnsynsetid	wnsynsetkey	mpcat40	mpcat40index
+1	wall	wall	8277	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+2	chair	chair	4646	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+22	books	book	1678	23	2	book	books	Books					n02870526	book.n.11	objects	39
+3	floor	floor	1553	2	5	floor	floor	Floor					n03365592	floor.n.01	floor	2
+5	door	door	1483	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+1163	object	object	1313	40	7		otherprop	Objects							objects	39
+16	window	window	1209	9	13	window	window	Window					n04587648	window.n.01	window	9
+4	table	table	1170	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+56	trash can	trash can	1090	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+13	pillow	pillow	937	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+15	picture	picture	862	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+41	ceiling	ceiling	806	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+26	box	box	775	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+161	doorframe	doorframe	768	8	12	door	door	Wall	door					doorframe.n.01	door	4
+19	monitor	monitor	765	40	7	monitor	otherprop	Objects	monitor	monitor	tv or monitor	3211117	n03782190	monitor.n.04	objects	39
+7	cabinet	cabinet	731	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+9	desk	desk	680	14	10	desk	desk	Table	desk	desk	table	4379243	n03179701	desk.n.01	table	5
+8	shelf	shelf	641	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+10	office chair	office chair	595	5	4	chair	chair	Chair	chair	chair	chair	3001627	n04373704	swivel_chair.n.01	chair	3
+31	towel	towel	570	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+6	couch	couch	502	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+14	sink	sink	488	34	7	sink	sink	Objects	sink				n04223580	sink.n.01	sink	15
+48	backpack	backpack	479	40	7	backpack	otherprop	Objects					n02769748	backpack.n.01	objects	39
+28	lamp	lamp	419	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+11	bed	bed	370	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+18	bookshelf	bookshelf	360	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+71	mirror	mirror	349	19	7	mirror	mirror	Objects					n03773035	mirror.n.01	mirror	21
+21	curtain	curtain	347	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+40	plant	plant	331	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+52	whiteboard	whiteboard	327	30	7	whiteboard	whiteboard	Objects					n03211616	display_panel.n.01	board_panel	35
+96	radiator	radiator	322	39	6	radiator	otherfurniture	Furniture					n04041069	radiator.n.02	misc	40
+22	book	book	318	23	2	book	books	Books					n02870526	book.n.11	objects	39
+29	kitchen cabinet	kitchen cabinet	310	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+49	toilet paper	toilet paper	291	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+29	kitchen cabinets	kitchen cabinet	289	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+23	armchair	armchair	281	5	4	chair	chair	Chair	chair	chair	chair	3001627	n02738535	armchair.n.01	chair	3
+63	shoes	shoe	272	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+24	coffee table	coffee table	258	7	10	coffee table	table	Table	table	table	table	4379243	n03063968	coffee_table.n.01	table	5
+17	toilet	toilet	256	33	7	toilet	toilet	Objects	toilet	toilet			n04446276	toilet.n.01	toilet	18
+47	bag	bag	252	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+32	clothes	clothes	248	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+46	keyboard	keyboard	246	40	7	keyboard	otherprop	Objects	keyboard		computer keyboard	3085013	n03085013	computer_keyboard.n.01	objects	39
+65	bottle	bottle	226	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+97	recycling bin	recycling bin	225	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+34	nightstand	nightstand	224	32	6	night stand	night stand	Furniture	night_stand	night_stand			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+38	stool	stool	221	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+33	tv	tv	219	25	11	television	television	TV			tv or monitor	3211117	n03211117	display.n.06	tv_monitor	22
+75	file cabinet	file cabinet	217	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+36	dresser	dresser	213	17	6	dresser	dresser	Furniture	dresser	dresser			n03015254	chest_of_drawers.n.01	chest_of_drawers	13
+64	computer tower	computer tower	203	40	7	computer	otherprop	Objects					n03082979	computer.n.01	objects	39
+32	clothing	clothes	165	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+101	telephone	telephone	164	40	7	telephone	otherprop	Objects			telephone	4401088	n04401088	telephone.n.01	objects	39
+130	cup	cup	157	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+27	refrigerator	refrigerator	154	24	6	refridgerator	refridgerator	Furniture					n04070727	refrigerator.n.01	appliances	37
+44	end table	end table	147	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+131	jacket	jacket	146	40	7	jacket	otherprop	Objects					n03589791	jacket.n.01	clothes	38
+55	shower curtain	shower curtain	144	28	7	shower curtain	shower curtain	Objects	curtain				n04209239	shower_curtain.n.01	curtain	12
+42	bathtub	bathtub	144	36	7	bathtub	bathtub	Objects	bathtub	bathtub	tub	2808440	n02808440	bathtub.n.01	bathtub	25
+59	microwave	microwave	141	40	7	microwave	otherprop	Objects			microwave	3761084	n03761084	microwave.n.02	appliances	37
+159	kitchen counter	kitchen counter	140	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+74	sofa chair	sofa chair	129	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+82	paper towel dispenser	paper towel dispenser	129	40	7	paper towel dispenser	otherprop	Objects							objects	39
+1164	bathroom vanity	bathroom vanity	126	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	table	5
+93	suitcase	suitcase	118	40	7	luggage	otherprop	Objects					n02773838	bag.n.06	objects	39
+77	laptop	laptop	111	40	7	laptop	otherprop	Objects	laptop		laptop	3642806	n03642806	laptop.n.01	objects	39
+67	ottoman	ottoman	111	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+128	shower walls	shower wall	109	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+50	printer	printer	106	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+35	counter	counter	104	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+69	board	board	100	38	7	board	otherstructure	Objects							board_panel	35
+100	soap dispenser	soap dispenser	99	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+62	stove	stove	95	38	7	stove	otherstructure	Objects			stove	4330267	n04330267	stove.n.02	appliances	37
+105	light	light	93	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+1165	closet wall	closet wall	90	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+165	mini fridge	mini fridge	87	24	6	refridgerator	refridgerator	Furniture					n03273913	electric_refrigerator.n.01	appliances	37
+7	cabinets	cabinet	79	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+5	doors	door	76	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+76	fan	fan	75	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+230	tissue box	tissue box	73	40	7	tissue box	otherprop	Objects					n02883344	box.n.01	objects	39
+54	blanket	blanket	72	40	7	blanket	otherprop	Objects					n02849154	blanket.n.01	objects	39
+125	bathroom stall	bathroom stall	71	38	7		otherstructure	Objects					n02873839	booth.n.02	misc	40
+72	copier	copier	70	40	7		otherprop	Objects					n03257586	duplicator.n.01	appliances	37
+68	bench	bench	66	39	6	bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+145	bar	bar	66	38	7	bar	otherstructure	Objects					n02788689	bar.n.03	misc	40
+157	soap dish	soap dish	65	40	7	soap dish	otherprop	Objects					n04254009	soap_dish.n.01	objects	39
+1166	laundry hamper	laundry hamper	65	40	7	laundry basket	otherprop	Objects							objects	39
+132	storage bin	storage bin	63	40	7	storage bin	otherprop	Objects							objects	39
+1167	bathroom stall door	bathroom stall door	62	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+232	light switch	light switch	61	38	7	light switch	otherstructure	Objects					n04372370	switch.n.01	misc	40
+134	coffee maker	coffee maker	61	40	7		otherprop	Objects					n03063338	coffee_maker.n.01	appliances	37
+51	tv stand	tv stand	61	39	6	tv stand	otherfurniture	Furniture	tv_stand				n03290653	entertainment_center.n.01	furniture	36
+250	decoration	decoration	60	40	7		otherprop	Objects					n03169390	decoration.n.01	misc	40
+1168	ceiling light	ceiling light	59	38	7	light	otherstructure	Objects					n03665366	light.n.02	lighting	28
+342	range hood	range hood	59	38	7	range hood	otherstructure	Objects	range_hood				n04053677	range_hood.n.01	misc	40
+89	blackboard	blackboard	58	38	7	blackboard	otherstructure	Objects					n02846511	blackboard.n.01	board_panel	35
+103	clock	clock	58	40	7	clock	otherprop	Objects			clock	3046257	n03046257	clock.n.01	objects	39
+99	wardrobe closet	wardrobe	54	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+95	rail	rail	53	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+154	bulletin board	bulletin board	53	38	7	board	otherstructure	Objects					n03211616	display_panel.n.01	board_panel	35
+140	mat	mat	52	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1169	trash bin	trash bin	52	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+193	ledge	ledge	51	38	7		otherstructure	Objects					n09337253	ledge.n.01	misc	40
+116	seat	seat	49	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+202	mouse	mouse	49	40	7	mouse	otherprop	Objects					n03793489	mouse.n.04	objects	39
+73	basket	basket	48	40	7	basket	otherprop	Objects			basket	2801938	n02801938	basket.n.01	objects	39
+78	shower	shower	48	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+1170	dumbbell	dumbbell	48	40	7		otherprop	Objects					n03255030	dumbbell.n.01	objects	39
+79	paper	paper	46	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+80	person	person	46	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+141	windowsill	windowsill	45	38	7		otherstructure	Objects					n04590263	windowsill.n.01	window	9
+57	closet	closet	45	39	6	wardrobe	otherfurniture	Furniture	wardrobe						misc	40
+102	bucket	bucket	45	40	7	bucket	otherprop	Objects					n02909870	bucket.n.01	misc	40
+261	sign	sign	44	40	7	sign	otherprop	Objects					n04217882	signboard.n.01	objects	39
+118	speaker	speaker	43	40	7	speaker	otherprop	Objects			speaker	3691459	n03691459	loudspeaker.n.01	objects	39
+136	dishwasher	dishwasher	43	38	7	dishwasher	otherstructure	Objects			dishwasher	3207941	n03207941	dishwasher.n.01	appliances	37
+98	container	container	43	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1171	stair rail	stair rail	42	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+170	shower curtain rod	shower curtain rod	42	40	7		otherprop	Objects							curtain	12
+1172	tube	tube	41	40	7		otherprop	Objects							misc	40
+1173	bathroom cabinet	bathroom cabinet	39	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+79	papers	paper	39	26	7	paper	paper	Objects					n14974264	paper.n.01	objects	39
+221	storage container	storage container	39	40	7	container	otherprop	Objects							objects	39
+570	paper bag	paper bag	39	37	7	bag	bag	Objects					n04122825	sack.n.01	objects	39
+138	paper towel roll	paper towel roll	39	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+168	ball	ball	39	40	7	ball	otherprop	Objects							objects	39
+276	closet doors	closet door	38	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+106	laundry basket	laundry basket	37	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+214	cart	cart	37	40	7	cart	otherprop	Objects					n03484083	handcart.n.01	shelving	31
+276	closet door	closet door	35	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+323	dish rack	dish rack	35	40	7	dish rack	otherprop	Objects					n03207630	dish_rack.n.01	objects	39
+58	stairs	stairs	35	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+86	blinds	blinds	35	13	13	blinds	blinds	Window					n02851099	blind.n.03	blinds	32
+2	stack of chairs	chair	35	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+399	purse	purse	34	40	7	purse	otherprop	Objects					n02774152	bag.n.04	objects	39
+121	bicycle	bicycle	33	40	7	bicycle	otherprop	Objects			bicycle	2834778	n02834778	bicycle.n.01	objects	39
+185	tray	tray	32	40	7	tray	otherprop	Objects					n04476259	tray.n.01	objects	39
+300	plunger	plunger	30	40	7		otherprop	Objects					n03970156	plunger.n.03	objects	39
+180	paper cutter	paper cutter	30	40	7	paper cutter	otherprop	Objects					n03886940	paper_cutter.n.01	objects	39
+163	toilet paper dispenser	toilet paper dispenser	29	40	7		otherprop	Objects							objects	39
+26	boxes	box	29	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+66	bin	bin	28	40	7	bin	otherprop	Objects					n02839910	bin.n.01	objects	39
+208	toilet seat cover dispenser	toilet seat cover dispenser	28	40	7		otherprop	Objects							objects	39
+112	guitar	guitar	28	40	7	guitar	otherprop	Objects	guitar		guitar	3467517	n03467517	guitar.n.01	objects	39
+540	mailboxes	mailbox	28	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+395	handicap bar	handicap bar	27	38	7	bar	otherstructure	Objects							misc	40
+166	fire extinguisher	fire extinguisher	27	40	7	fire extinguisher	otherprop	Objects					n03345837	fire_extinguisher.n.01	misc	40
+122	ladder	ladder	27	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	stairs	16
+120	column	column	26	38	7	column	otherstructure	Objects					n03074380	column.n.06	column	24
+107	pipe	pipe	25	40	7	pipe	otherprop	Objects					n03944672	pipe.n.02	misc	40
+283	vacuum cleaner	vacuum cleaner	25	40	7		otherprop	Objects					n04517823	vacuum.n.04	objects	39
+88	plate	plate	24	40	7	plate	otherprop	Objects					n03959485	plate.n.04	objects	39
+90	piano	piano	24	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+177	water cooler	water cooler	24	39	6	water cooler	otherfurniture	Furniture					n04559166	water_cooler.n.01	misc	40
+1174	cd case	cd case	24	40	7		otherprop	Objects							objects	39
+562	bowl	bowl	24	40	7	bowl	otherprop	Objects	bowl		bowl	2880940	n02880940	bowl.n.03	objects	39
+1175	closet rod	closet rod	24	40	7		otherprop	Objects					n04100174	rod.n.01	misc	40
+1156	bathroom counter	bathroom counter	24	12	6	counter	counter	Furniture	table	table	table	4379243	n03116530	counter.n.01	counter	26
+84	oven	oven	23	38	7	oven	otherstructure	Objects					n03862676	oven.n.01	appliances	37
+104	stand	stand	23	39	6	stand	otherfurniture	Furniture	table	table	table	4379243	n04301000	stand.n.04	table	5
+229	scale	scale	23	40	7	scale	otherprop	Objects					n04141975	scale.n.07	objects	39
+70	washing machine	washing machine	23	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+325	broom	broom	22	40	7	broom	otherprop	Objects					n02906734	broom.n.01	objects	39
+169	hat	hat	22	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+128	shower wall	shower wall	22	1	12	wall	wall	Wall					n04208936	shower.n.01	wall	1
+331	guitar case	guitar case	21	40	7	guitar case	otherprop	Objects							objects	39
+87	rack	rack	21	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+488	water pitcher	water pitcher	21	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+776	laundry detergent	laundry detergent	21	40	7		otherprop	Objects							objects	39
+370	hair dryer	hair dryer	21	40	7	hair dryer	otherprop	Objects					n03483316	hand_blower.n.01	objects	39
+191	pillar	pillar	21	38	7	column	otherstructure	Objects					n03073977	column.n.07	column	24
+748	divider	divider	20	40	7		otherprop	Objects							wall	1
+242	power outlet	power outlet	19	40	7		otherprop	Objects							misc	40
+45	dining table	dining table	19	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+417	shower floor	shower floor	19	2	5	floor	floor	Floor					n04208936	shower.n.01	floor	2
+70	washing machines	washing machine	19	39	6	washing machine	otherfurniture	Furniture			washing_machine	4554684	n04554684	washer.n.03	appliances	37
+188	shower door	shower door	19	8	12	door	door	Wall	door				n04208936	shower.n.01	door	4
+1176	coffee kettle	coffee kettle	18	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1177	wardrobe cabinet	wardrobe	18	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1178	structure	structure	18	38	7		otherstructure	Objects							misc	40
+18	bookshelves	bookshelf	17	10	6	bookshelf	bookshelf	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+110	clothes dryer	clothes dryer	17	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+148	toaster	toaster	17	40	7	toaster	otherprop	Objects					n04442312	toaster.n.02	appliances	37
+63	shoe	shoe	17	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+155	ironing board	ironing board	16	39	6	ironing board	otherfurniture	Furniture					n03586090	ironing_board.n.01	objects	39
+572	alarm clock	alarm clock	16	40	7	alarm clock	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+1179	shower head	shower head	15	38	7		otherstructure	Objects							shower	23
+28	lamp base	lamp	15	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+392	water bottle	water bottle	15	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n04557648	water_bottle.n.01	objects	39
+1180	keyboard piano	keyboard piano	15	39	6	piano	otherfurniture	Furniture	piano		piano	3928116	n03928116	piano.n.01	furniture	36
+609	projector screen	projector screen	15	38	7	projector screen	otherstructure	Objects							misc	40
+1181	case of water bottles	case of water bottles	15	40	7		otherprop	Objects							objects	39
+195	toaster oven	toaster oven	14	40	7	toaster oven	otherprop	Objects					n04442441	toaster_oven.n.01	appliances	37
+581	music stand	music stand	14	39	6	music stand	otherfurniture	Furniture					n03801760	music_stand.n.01	furniture	36
+58	staircase	stairs	14	38	7	stairs	otherstructure	Objects					n04298308	stairway.n.01	stairs	16
+1182	coat rack	coat rack	14	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	3
+1183	storage organizer	storage organizer	14	40	7		otherprop	Objects							shelving	3
+139	machine	machine	14	40	7	machine	otherprop	Objects					n03699975	machine.n.01	appliances	37
+1184	folded chair	folded chair	14	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1185	fire alarm	fire alarm	14	40	7		otherprop	Objects					n03343737	fire_alarm.n.02	misc	40
+156	fireplace	fireplace	13	38	7	fireplace	otherstructure	Objects					n03346455	fireplace.n.01	fireplace	27
+408	vent	vent	13	40	7		otherprop	Objects					n04526241	vent.n.01	misc	40
+213	furniture	furniture	13	39	6	furniture	otherfurniture	Furniture					n03405725	furniture.n.01	furniture	36
+1186	power strip	power strip	13	40	7		otherprop	Objects							objects	39
+1187	calendar	calendar	13	40	7		otherprop	Objects							objects	39
+1188	poster	poster	13	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+115	toilet paper holder	toilet paper holder	13	40	7	toilet paper holder	otherprop	Objects							objects	39
+1189	potted plant	potted plant	12	40	7	plant	otherprop	Objects	plant				n00017222	plant.n.02	plant	14
+304	stuffed animal	stuffed animal	12	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+1190	luggage	luggage	12	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+21	curtains	curtain	12	16	13	curtain	curtain	Window	curtain				n03151077	curtain.n.01	curtain	12
+312	headphones	headphones	12	40	7		otherprop	Objects					n03261776	earphone.n.01	objects	39
+233	crate	crate	12	39	6	crate	otherfurniture	Furniture					n03127925	crate.n.01	objects	39
+286	candle	candle	12	40	7	candle	otherprop	Objects	lamp				n02948072	candle.n.01	objects	39
+264	projector	projector	12	40	7	projector	otherprop	Objects					n04009552	projector.n.02	objects	39
+110	clothes dryers	clothes dryer	12	39	6		otherfurniture	Furniture					n03251766	dryer.n.01	appliances	37
+1191	mattress	mattress	12	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+356	dustpan	dustpan	12	40	7		otherprop	Objects					n03259009	dustpan.n.02	objects	39
+25	drawer	drawer	11	39	6	drawer	otherfurniture	Furniture					n03233905	drawer.n.01	furniture	36
+750	rod	rod	11	40	7		otherprop	Objects			pistol	3948459	n03427202	gat.n.01	misc	40
+269	globe	globe	11	40	7	globe	otherprop	Objects							objects	39
+307	footrest	footrest	11	39	6	foot rest	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+410	piano bench	piano bench	11	39	6	piano bench	otherfurniture	Furniture	bench		bench	2828884	n02828884	bench.n.01	seating	34
+730	breakfast bar	breakfast bar	11	38	7	bar	otherstructure	Objects							counter	26
+216	step stool	step stool	11	40	7	step stool	otherprop	Objects	stool				n04315713	step_stool.n.01	stool	19
+1192	hand rail	hand rail	11	38	7	railing	otherstructure	Objects							railing	30
+119	vending machine	vending machine	11	40	7	machine	otherprop	Objects					n04525305	vending_machine.n.01	appliances	37
+682	ceiling fan	ceiling fan	11	40	7	fan	otherprop	Objects					n03320046	fan.n.01	misc	40
+434	swiffer	swiffer	11	40	7		otherprop	Objects							objects	39
+126	foosball table	foosball table	11	39	6	foosball table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+919	jar	jar	11	40	7	jar	otherprop	Objects			jar	3593526	n03593526	jar.n.01	objects	39
+85	footstool	footstool	11	39	6	ottoman	otherfurniture	Furniture	stool				n03380724	footstool.n.01	stool	19
+1193	folded table	folded table	10	7	10	table	table	Table	table	table	table	4379243	n04379243	table.n.02	table	5
+108	round table	round table	10	7	10	table	table	Table	table	table	table	4379243	n04114554	round_table.n.02	table	5
+135	hamper	hamper	10	40	7	basket	otherprop	Objects			basket	2801938	n03482405	hamper.n.02	objects	39
+1194	poster tube	poster tube	10	40	7		otherprop	Objects							objects	39
+432	case	case	10	40	7	case	otherprop	Objects							objects	39
+53	carpet	carpet	10	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+1195	thermostat	thermostat	10	40	7		otherprop	Objects					n04422875	thermostat.n.01	misc	40
+111	coat	coat	10	40	7	jacket	otherprop	Objects					n03057021	coat.n.01	clothes	38
+305	water fountain	water fountain	10	38	7	water fountain	otherstructure	Objects					n03241335	drinking_fountain.n.01	misc	40
+1125	smoke detector	smoke detector	10	40	7		otherprop	Objects							misc	40
+13	pillows	pillow	9	18	7	pillow	pillow	Objects			pillow	3938244	n03938244	pillow.n.01	cushion	8
+1196	flip flops	flip flops	9	40	7	shoe	otherprop	Objects					n04199027	shoe.n.01	clothes	38
+1197	cloth	cloth	9	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1198	banner	banner	9	40	7		otherprop	Objects					n02788021	banner.n.01	misc	40
+1199	clothes hanger	clothes hanger	9	40	7		otherprop	Objects					n03057920	coat_hanger.n.01	objects	39
+1200	whiteboard eraser	whiteboard eraser	9	40	7		otherprop	Objects							objects	39
+378	iron	iron	9	40	7		otherprop	Objects					n03584829	iron.n.04	objects	39
+591	instrument case	instrument case	9	40	7	case	otherprop	Objects							objects	39
+49	toilet paper rolls	toilet paper	9	40	7	toilet paper	otherprop	Objects					n15075141	toilet_tissue.n.01	objects	39
+92	soap	soap	9	40	7	soap	otherprop	Objects					n04253437	soap.n.01	objects	39
+1098	block	block	9	40	7		otherprop	Objects							misc	40
+291	wall hanging	wall hanging	8	40	7		otherprop	Objects					n03491178	hanging.n.01	picture	6
+1063	kitchen island	kitchen island	8	38	7	kitchen island	otherstructure	Objects					n03620600	kitchen_island.n.01	counter	26
+107	pipes	pipe	8	38	7		otherstructure	Objects							misc	40
+1135	toothbrush	toothbrush	8	40	7	toothbrush	otherprop	Objects					n04453156	toothbrush.n.01	objects	39
+189	shirt	shirt	8	40	7		otherprop	Objects					n04197391	shirt.n.01	clothes	38
+245	cutting board	cutting board	8	40	7	cutting board	otherprop	Objects					n03025513	chopping_board.n.01	objects	39
+194	vase	vase	8	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1201	shower control valve	shower control valve	8	38	7		otherstructure	Objects					n04208936	shower.n.01	shower	23
+386	exercise machine	exercise machine	8	40	7	machine	otherprop	Objects							gym_equipment	33
+1202	compost bin	compost bin	8	39	6	garbage bin	otherfurniture	Furniture			trash_bin	2747177	n02747177	ashcan.n.01	objects	39
+857	shorts	shorts	8	40	7	shorts	otherprop	Objects							clothes	38
+452	tire	tire	8	40	7		otherprop	Objects					n04440749	tire.n.01	objects	39
+1203	teddy bear	teddy bear	7	40	7	stuffed animal	otherprop	Objects					n04399382	teddy.n.01	objects	39
+346	bathrobe	bathrobe	7	40	7		otherprop	Objects					n02807616	bathrobe.n.01	clothes	38
+152	handrail	handrail	7	38	7	railing	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+83	faucet	faucet	7	40	7	faucet	otherprop	Objects			faucet	3325088	n03325088	faucet.n.01	misc	40
+1204	pantry wall	pantry wall	7	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+726	thermos	thermos	7	40	7	flask	otherprop	Objects	bottle		bottle	2876657	n04422727	thermos.n.01	objects	39
+61	rug	rug	7	40	7	rug	otherprop	Objects					n04118021	rug.n.01	floor	2
+39	couch cushions	cushion	7	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1117	tripod	tripod	7	39	6	stand	otherfurniture	Furniture					n04485082	tripod.n.01	objects	39
+540	mailbox	mailbox	7	29	7	box	box	Objects			mailbox	3710193	n03710193	mailbox.n.01	misc	40
+1205	tupperware	tupperware	7	40	7		otherprop	Objects							objects	39
+415	shoe rack	shoe rack	7	40	7	shoe rack	otherprop	Objects							shelving	31
+31	towels	towel	6	27	7	towel	towel	Objects					n04459362	towel.n.01	towel	20
+1206	beer bottles	beer bottle	6	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+153	treadmill	treadmill	6	39	6	treadmill	otherfurniture	Furniture					n04477387	treadmill.n.01	gym_equipment	33
+1207	salt	salt	6	40	7		otherprop	Objects							objects	39
+129	chest	chest	6	39	6	chest	otherfurniture	Furniture	dresser	dresser					chest_of_drawers	13
+220	dispenser	dispenser	6	40	7		otherprop	Objects					n03210683	dispenser.n.01	objects	39
+1208	mirror doors	mirror door	6	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+231	remote	remote	6	40	7		otherprop	Objects			remote_control	4074963	n04074963	remote_control.n.01	objects	39
+1209	folded ladder	folded ladder	6	39	6	ladder	otherfurniture	Furniture	stairs				n03632277	ladder.n.01	misc	40
+39	cushion	cushion	6	18	7	pillow	pillow	Objects					n03151500	cushion.n.03	cushion	8
+1210	carton	carton	6	40	7		otherprop	Objects							objects	39
+117	step	step	6	38	7		otherstructure	Objects					n04314914	step.n.04	misc	40
+822	drying rack	drying rack	6	39	6	drying rack	otherfurniture	Furniture							shelving	31
+238	slippers	slipper	6	40	7	shoe	otherprop	Objects					n04241394	slipper.n.01	clothes	38
+143	pool table	pool table	6	39	6	pool table	otherfurniture	Furniture	table	table	table	4379243	n03982430	pool_table.n.01	table	5
+1211	soda stream	soda stream	6	40	7		otherprop	Objects							objects	39
+228	toilet brush	toilet brush	6	40	7	toilet brush	otherprop	Objects							objects	39
+494	loft bed	loft bed	6	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+226	cooking pot	cooking pot	6	40	7	pot	otherprop	Objects							objects	39
+91	heater	heater	6	39	6	heater	otherfurniture	Furniture					n03508101	heater.n.01	misc	40
+1072	messenger bag	messenger bag	6	37	7	bag	bag	Objects							objects	39
+435	stapler	stapler	6	40	7	stapler	otherprop	Objects					n04303497	stapler.n.01	objects	39
+1165	closet walls	closet wall	5	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+345	scanner	scanner	5	40	7		otherprop	Objects							appliances	37
+893	elliptical machine	elliptical machine	5	40	7	machine	otherprop	Objects							gym_equipment	33
+621	kettle	kettle	5	40	7	pot	otherprop	Objects					n03612814	kettle.n.01	objects	39
+1212	metronome	metronome	5	40	7		otherprop	Objects					n03757604	metronome.n.01	objects	39
+297	dumbell	dumbell	5	40	7		otherprop	Objects							objects	39
+1213	music book	music book	5	23	2	book	books	Books					n02870526	book.n.11	objects	39
+1214	rice cooker	rice cooker	5	40	7		otherprop	Objects							objects	39
+1215	dart board	dart board	5	38	7	board	otherstructure	Objects					n03162940	dartboard.n.01	objects	39
+529	sewing machine	sewing machine	5	40	7	sewing machine	otherprop	Objects					n04179913	sewing_machine.n.01	objects	39
+1216	grab bar	grab bar	5	38	7	railing	otherstructure	Objects							railing	30
+1217	flowerpot	flowerpot	5	40	7	vase	otherprop	Objects	vase		jar	3593526	n04522168	vase.n.01	objects	39
+1218	painting	painting	5	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1219	railing	railing	5	38	7	railing	otherstructure	Objects					n04047401	railing.n.01	railing	30
+1220	stair	stair	5	38	7	stairs	otherstructure	Objects	stairs				n04314914	step.n.04	stairs	16
+525	toolbox	toolbox	5	39	6	chest	otherfurniture	Furniture					n04452615	toolbox.n.01	objects	39
+204	nerf gun	nerf gun	5	40	7		otherprop	Objects							objects	39
+693	binders	binder	5	40	7	binder	otherprop	Objects							objects	39
+179	desk lamp	desk lamp	5	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1221	quadcopter	quadcopter	5	40	7		otherprop	Objects							objects	39
+1222	pitcher	pitcher	5	40	7	pitcher	otherprop	Objects					n03950228	pitcher.n.02	objects	39
+1223	hanging	hanging	5	40	7		otherprop	Objects							misc	40
+1224	mail	mail	5	40	7		otherprop	Objects							misc	40
+1225	closet ceiling	closet ceiling	5	22	3	ceiling	ceiling	Ceiling					n02990373	ceiling.n.01	ceiling	17
+1226	hoverboard	hoverboard	5	40	7		otherprop	Objects							objects	39
+1227	beanbag chair	beanbag chair	5	39	6	bean bag	otherfurniture	Furniture					n02816656	beanbag.n.01	chair	3
+571	water heater	water heater	5	40	7	water heater	otherprop	Objects					n04560113	water_heater.n.01	misc	40
+1228	spray bottle	spray bottle	5	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+556	rope	rope	5	40	7	rope	otherprop	Objects					n04108268	rope.n.01	objects	39
+280	plastic container	plastic container	5	40	7	container	otherprop	Objects							objects	39
+1229	soap bottle	soap bottle	5	40	7	soap	otherprop	Objects							objects	39
+1230	ikea bag	ikea bag	4	37	7	bag	bag	Objects				2773838	n02773838	bag.n.06	objects	39
+1231	sleeping bag	sleeping bag	4	40	7		otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+1232	duffel bag	duffel bag	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+746	frying pan	frying pan	4	40	7	frying pan	otherprop	Objects					n03400231	frying_pan.n.01	objects	39
+1233	oven mitt	oven mitt	4	40	7		otherprop	Objects							objects	39
+1234	pot	pot	4	40	7	pot	otherprop	Objects					n04235860	sleeping_bag.n.01	objects	39
+144	hand dryer	hand dryer	4	40	7		otherprop	Objects							objects	39
+282	dollhouse	dollhouse	4	39	6	doll house	otherfurniture	Furniture					n03219483	dollhouse.n.01	objects	39
+167	shampoo bottle	shampoo bottle	4	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1235	hair brush	hair brush	4	40	7		otherprop	Objects					n02908217	brush.n.02	objects	39
+1236	tennis racket	tennis racket	4	40	7		otherprop	Objects					n04409806	tennis_racket.n.01	objects	39
+1237	display case	display case	4	40	7	case	otherprop	Objects							objects	39
+234	ping pong table	ping pong table	4	39	6	ping pong table	otherfurniture	Furniture	table	table	table	4379243	n04379243	table.n.02	table	5
+563	boiler	boiler	4	40	7		otherprop	Objects							misc	40
+1238	bag of coffee beans	bag of coffee beans	4	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+1239	bananas	banana	4	40	7		otherprop	Objects					n00021265	food.n.01	objects	39
+1240	carseat	carseat	4	40	7		otherprop	Objects							misc	40
+366	helmet	helmet	4	40	7		otherprop	Objects			helmet	3513137	n03513137	helmet.n.02	clothes	38
+816	umbrella	umbrella	4	40	7	umbrella	otherprop	Objects					n04507155	umbrella.n.01	objects	39
+1241	coffee box	coffee box	4	40	7		otherprop	Objects							objects	39
+719	envelope	envelope	4	40	7	envelope	otherprop	Objects					n03291819	envelope.n.01	objects	39
+284	wet floor sign	wet floor sign	4	40	7	sign	otherprop	Objects							misc	40
+1242	clothing rack	clothing rack	4	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+247	controller	controller	4	40	7		otherprop	Objects					n03096960	control.n.09	objects	39
+1243	bath walls	bathroom wall	4	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+1244	podium	podium	4	39	6		otherfurniture	Furniture					n03159640	dais.n.01	furniture	36
+1245	storage box	storage box	4	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1246	dolly	dolly	4	40	7		otherprop	Objects							misc	40
+1247	shampoo	shampoo	3	40	7		otherprop	Objects					n04183516	shampoo.n.01	objects	39
+592	paper tray	paper tray	3	40	7	paper tray	otherprop	Objects							objects	39
+385	cabinet door	cabinet door	3	8	12	door	door	Wall	door						door	4
+1248	changing station	changing station	3	40	7		otherprop	Objects							misc	40
+1249	poster printer	poster printer	3	40	7	printer	otherprop	Objects			printer	4004475	n04004475	printer.n.03	appliances	37
+133	screen	screen	3	40	7		otherprop	Objects					n03151077	curtain.n.01	curtain	12
+301	soap bar	soap bar	3	38	7	bar	otherstructure	Objects							objects	39
+1250	crutches	crutches	3	40	7		otherprop	Objects					n03141823	crutch.n.01	objects	39
+379	studio light	studio light	3	38	7	light	otherstructure	Objects							lighting	28
+130	stack of cups	cup	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+1251	toilet flush button	toilet flush button	3	40	7		otherprop	Objects							objects	39
+450	trunk	trunk	3	40	7		otherprop	Objects							misc	40
+1252	grocery bag	grocery bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03461288	grocery_bag.n.01	objects	39
+316	plastic bin	plastic bin	3	40	7	bin	otherprop	Objects							objects	39
+1253	pizza box	pizza box	3	29	7	box	box	Objects							objects	39
+385	cabinet doors	cabinet door	3	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	door	4
+1254	legs	legs	3	31	7	person	person	Objects	person				n05217688	person.n.02	misc	40
+461	car	car	3	40	7	car	otherprop	Objects	car		car	2958343	n02958343	car.n.01	misc	40
+1255	shaving cream	shaving cream	3	40	7		otherprop	Objects					n04186051	shaving_cream.n.01	objects	39
+1256	luggage stand	luggage stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+599	shredder	shredder	3	40	7		otherprop	Objects					n04210120	shredder.n.01	objects	39
+281	statue	statue	3	40	7	sculpture	otherprop	Objects					n04306847	statue.n.01	misc	40
+1257	urinal	urinal	3	33	7	toilet	toilet	Objects	toilet	toilet			n04515991	urinal.n.01	toilet	18
+1258	hose	hose	3	40	7		otherprop	Objects					n03539875	hose.n.03	misc	40
+1259	bike pump	bike pump	3	40	7		otherprop	Objects							objects	39
+319	coatrack	coatrack	3	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1260	bear	bear	3	40	7		otherprop	Objects							objects	39
+28	wall lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+1261	humidifier	humidifier	3	40	7		otherprop	Objects							objects	39
+546	toothpaste	toothpaste	3	40	7	toothpaste	otherprop	Objects							objects	39
+1262	mouthwash bottle	mouthwash bottle	3	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1263	poster cutter	poster cutter	3	40	7		otherprop	Objects							objects	39
+1264	golf bag	golf bag	3	37	7	bag	bag	Objects			suitcase	2773838	n03445617	golf_bag.n.01	objects	39
+1265	food container	food container	3	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1266	camera	camera	3	40	7		otherprop	Objects							objects	39
+28	table lamp	lamp	3	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n04380533	table_lamp.n.01	lighting	28
+1267	yoga mat	yoga mat	3	20	5	floor mat	floor mat	Floor					n03727837	mat.n.01	floor	2
+1268	card	card	3	40	7		otherprop	Objects							objects	39
+1269	mug	mug	3	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+188	shower doors	shower door	3	38	7		otherstructure	Objects					n04208936	shower.n.01	door	4
+689	cardboard	cardboard	3	40	7		otherprop	Objects							objects	39
+1270	rack stand	rack stand	3	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+1271	boxes of paper	boxes of paper	3	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1272	flag	flag	3	40	7		otherprop	Objects							misc	40
+354	futon	futon	3	39	6	mattress	otherfurniture	Furniture					n03408444	futon.n.01	sofa	10
+339	magazine	magazine	3	40	7	magazine	otherprop	Objects					n06595351	magazine.n.01	objects	39
+1009	exit sign	exit sign	3	40	7	exit sign	otherprop	Objects							misc	40
+1273	rolled poster	rolled poster	3	40	7		otherprop	Objects							objects	39
+1274	wheel	wheel	3	40	7		otherprop	Objects							objects	39
+15	pictures	picture	3	11	8	picture	picture	Picture					n03931044	picture.n.01	picture	6
+1275	blackboard eraser	blackboard eraser	3	40	7	eraser	otherprop	Objects					n03294833	eraser.n.01	objects	39
+361	organizer	organizer	3	40	7		otherprop	Objects					n03918737	personal_digital_assistant.n.01	objects	39
+1276	doll	doll	3	40	7	toy	otherprop	Objects					n03219135	doll.n.01	objects	39
+326	book rack	book rack	3	39	6	bookrack	otherfurniture	Furniture							objects	39
+1277	laundry bag	laundry bag	3	40	7	laundry basket	otherprop	Objects			basket	2801938	n03050864	clothes_hamper.n.01	objects	39
+1278	sponge	sponge	3	40	7		otherprop	Objects					n01906749	sponge.n.04	objects	39
+116	seating	seat	3	39	6	furniture	otherfurniture	Furniture					n04161981	seat.n.03	furniture	36
+1184	folded chairs	folded chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1279	lotion bottle	lotion bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+212	can	can	2	40	7	can	otherprop	Objects			can	2946921	n02946921	can.n.01	objects	39
+1280	lunch box	lunch box	2	40	7		otherprop	Objects							objects	39
+1281	food display	food display	2	40	7		otherprop	Objects							misc	40
+794	storage shelf	storage shelf	2	40	7		otherprop	Objects							shelving	31
+1282	sliding wood door	sliding wood door	2	40	7		otherprop	Objects							door	4
+955	pants	pants	2	40	7		otherprop	Objects					n04489008	trouser.n.01	clothes	38
+387	wood	wood	2	40	7		otherprop	Objects							misc	40
+69	boards	board	2	38	7	board	otherstructure	Objects							board_panel	35
+65	bottles	bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+523	washcloth	washcloth	2	40	7		otherprop	Objects					n04554523	washcloth.n.01	towel	20
+389	workbench	workbench	2	39	6	bench	otherfurniture	Furniture	bench		table	4379243	n04600486	workbench.n.01	table	5
+29	open kitchen cabinet	kitchen cabinet	2	3	6	cabinet	cabinet	Furniture					n02933112	cabinet.n.01	cabinet	7
+1283	organizer shelf	organizer shelf	2	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+146	frame	frame	2	38	7		otherstructure	Objects							misc	40
+130	cups	cup	2	40	7	cup	otherprop	Objects	cup		cup or mug	3797390	n03797390	mug.n.04	objects	39
+372	exercise ball	exercise ball	2	40	7	ball	otherprop	Objects					n04285146	sports_equipment.n.01	gym_equipment	33
+289	easel	easel	2	39	6	stand	otherfurniture	Furniture					n03262809	easel.n.01	furniture	36
+440	garbage bag	garbage bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+321	roomba	roomba	2	40	7		otherprop	Objects							objects	39
+976	garage door	garage door	2	38	7	garage door	otherstructure	Objects	door						door	4
+1256	luggage rack	luggage stand	2	39	6	stand	otherfurniture	Furniture					n04038440		shelving	31
+1284	bike lock	bike lock	2	40	7		otherprop	Objects							objects	39
+1285	briefcase	briefcase	2	40	7		otherprop	Objects					n02900705	briefcase.n.01	objects	39
+357	hand towel	hand towel	2	27	7	towel	towel	Objects					n03490006	hand_towel.n.01	towel	20
+1286	bath products	bath product	2	40	7		otherprop	Objects							objects	39
+1287	star	star	2	40	7		otherprop	Objects					n09444783	star.n.03	misc	40
+365	map	map	2	40	7	map	otherprop	Objects					n03720163	map.n.01	misc	40
+1288	coffee bean bag	coffee bean bag	2	37	7	bag	bag	Objects			suitcase	2773838	n02773838	bag.n.06	objects	39
+81	headboard	headboard	2	39	6	headboard	otherfurniture	Furniture					n03502200	headboard.n.01	bed	11
+1289	ipad	ipad	2	40	7		otherprop	Objects							objects	39
+1290	display rack	display rack	2	39	6	stand	otherfurniture	Furniture					n04038440	rack.n.05	shelving	31
+948	traffic cone	traffic cone	2	40	7	cone	otherprop	Objects	cone						objects	39
+174	toiletry	toiletry	2	40	7		otherprop	Objects					n04447443	toiletry.n.01	objects	39
+1028	canopy	canopy	2	40	7		otherprop	Objects							misc	40
+1291	massage chair	massage chair	2	5	4	chair	chair	Chair	chair	chair	chair	3001627	n03001627	chair.n.01	chair	3
+1292	paper organizer	paper organizer	2	40	7		otherprop	Objects							objects	39
+1005	barricade	barricade	2	40	7		otherprop	Objects							misc	40
+235	platform	platform	2	38	7		otherstructure	Objects							misc	40
+1293	cap	cap	2	40	7	hat	otherprop	Objects					n03497657	hat.n.01	clothes	38
+1294	dumbbell plates	dumbbell plates	2	40	7		otherprop	Objects							objects	39
+1295	elevator	elevator	2	38	7		otherstructure	Objects							misc	40
+1296	cooking pan	cooking pan	2	40	7	pan	otherprop	Objects					n03880531	pan.n.01	objects	39
+1297	trash bag	trash bag	2	37	7	bag	bag	Objects							objects	39
+1298	santa	santa	2	40	7		otherprop	Objects							misc	40
+1299	jewelry box	jewelry box	2	29	7	box	box	Objects					n02883344	box.n.01	objects	39
+1300	boat	boat	2	40	7		otherprop	Objects							misc	40
+1301	sock	sock	2	21	7	clothes	clothes	Objects					n04254777	sock.n.01	clothes	38
+1051	kinect	kinect	2	40	7	kinect	otherprop	Objects							objects	39
+566	crib	crib	2	39	6	crib	otherfurniture	Furniture							furniture	36
+1302	plastic storage bin	plastic storage bin	2	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1062	cooler	cooler	2	24	6	refridgerator	refridgerator	Furniture					n03102654	cooler.n.01	appliances	37
+1303	kitchen apron	kitchen apron	2	21	7	clothes	clothes	Objects					n02728440	apparel.n.01	clothes	38
+1304	dishwashing soap bottle	dishwashing soap bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1305	xbox controller	xbox controller	2	40	7		otherprop	Objects							objects	39
+1306	banana holder	banana holder	2	40	7		otherprop	Objects							objects	39
+298	ping pong paddle	ping pong paddle	2	40	7		otherprop	Objects							table	5
+1307	airplane	airplane	2	40	7		otherprop	Objects							misc	40
+1308	conditioner bottle	conditioner bottle	2	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+1309	tea kettle	tea kettle	2	40	7	tea kettle	otherprop	Objects					n04397768	teakettle.n.01	objects	39
+43	bedframe	bedframe	2	39	6		otherfurniture	Furniture					n02822579	bedstead.n.01	bed	11
+1310	wood beam	wood beam	2	38	7		otherstructure	Objects							beam	29
+593	toilet paper package	toilet paper package	2	40	7		otherprop	Objects							objects	39
+1311	wall mounted coat rack	wall mounted coat rack	2	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+1312	film light	film light	2	40	7		otherprop	Objects							lighting	28
+749	ceiling lamp	ceiling lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+623	chain	chain	1	40	7		otherprop	Objects							chair	3
+1313	sofa	sofa	1	6	9	sofa	sofa	Sofa	sofa	sofa	sofa	4256520	n04256520	sofa.n.01	sofa	10
+99	closet wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+265	sweater	sweater	1	40	7		otherprop	Objects					n04370048	sweater.n.01	clothes	38
+1314	kitchen mixer	kitchen mixer	1	40	7		otherprop	Objects							appliances	37
+99	wardrobe	wardrobe	1	39	6	wardrobe	otherfurniture	Furniture	wardrobe				n04550184	wardrobe.n.01	furniture	36
+1315	water softener	water softener	1	40	7		otherprop	Objects							misc	40
+448	banister	banister	1	38	7	banister	otherstructure	Objects					n02788148	bannister.n.02	railing	30
+257	trolley	trolley	1	40	7	trolley	otherprop	Objects					n04335435	streetcar.n.01	misc	40
+1316	pantry shelf	pantry shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
+786	sofa bed	sofa bed	1	4	1	bed	bed	Bed	bed	bed	bed	2818832	n02818832	bed.n.01	bed	11
+801	loofa	loofa	1	40	7		otherprop	Objects							objects	39
+972	shower faucet handle	shower faucet handle	1	40	7	handle	otherprop	Objects							shower	23
+1317	toy piano	toy piano	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1318	fish	fish	1	40	7		otherprop	Objects					n02512053	fish.n.01	objects	39
+75	file cabinets	file cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n03337140	file.n.03	cabinet	7
+657	cat litter box	cat litter box	1	29	7	box	box	Objects							objects	39
+561	electric panel	electric panel	1	40	7		otherprop	Objects							misc	40
+93	suitcases	suitcase	1	40	7	luggage	otherprop	Objects					n02774630	baggage.n.01	objects	39
+513	curtain rod	curtain rod	1	38	7	curtain rod	otherstructure	Objects							curtain	12
+411	bunk bed	bunk bed	1	39	6	bunk bed	otherfurniture	Furniture	bed	bed	bed	2818832	n02920259	bunk_bed.n.01	bed	11
+1122	chandelier	chandelier	1	38	7	chandelier	otherstructure	Objects					n03005285	chandelier.n.01	lighting	28
+922	tape	tape	1	40	7	tape	otherprop	Objects							objects	39
+88	plates	plate	1	40	7		otherprop	Objects					n03959485	plate.n.04	objects	39
+518	alarm	alarm	1	40	7	alarm	otherprop	Objects			clock	3046257	n02694662	alarm_clock.n.01	objects	39
+814	fire hose	fire hose	1	40	7		otherprop	Objects					n03346004	fire_hose.n.01	misc	40
+1319	toy dinosaur	toy dinosaur	1	40	7	toy	otherprop	Objects					n03964744	plaything.n.01	objects	39
+1320	cone	cone	1	40	7		otherprop	Objects							objects	39
+649	glass doors	glass door	1	8	12	door	door	Wall	door				n03221720	door.n.01	door	4
+607	hatrack	hatrack	1	40	7		otherprop	Objects					n03059103	coatrack.n.01	shelving	31
+819	subwoofer	subwoofer	1	40	7	speaker	otherprop	Objects			speaker	3691459	n04349401	subwoofer.n.01	objects	39
+1321	fire sprinkler	fire sprinkler	1	40	7		otherprop	Objects							misc	40
+1322	trash cabinet	trash cabinet	1	3	6	cabinet	cabinet	Furniture			cabinet	2933112	n02933112	cabinet.n.01	cabinet	7
+1204	pantry walls	pantry wall	1	1	12	wall	wall	Wall					n04546855	wall.n.01	wall	1
+227	photo	photo	1	40	7	photo	otherprop	Objects					n03925226	photograph.n.01	picture	6
+817	barrier	barrier	1	40	7		otherprop	Objects					n02796623	barrier.n.01	misc	40
+130	stacks of cups	cup	1	40	7		otherprop	Objects					n03147509	cup.n.01	objects	39
+712	beachball	beachball	1	40	7	ball	otherprop	Objects					n02814224	beach_ball.n.01	objects	39
+1323	folded boxes	folded boxes	1	40	7		otherprop	Objects							objects	39
+1324	contact lens solution bottle	contact lens solution bottle	1	40	7	bottle	otherprop	Objects	bottle		bottle	2876657	n02876657	bottle.n.01	objects	39
+673	covered box	covered box	1	29	7	box	box	Objects							objects	39
+459	folder	folder	1	40	7	folder	otherprop	Objects					n03376279	folder.n.02	objects	39
+643	mail trays	mail tray	1	40	7	mail tray	otherprop	Objects							objects	39
+238	slipper	slipper	1	40	7		otherprop	Objects					n04241394	slipper.n.01	clothes	38
+765	magazine rack	magazine rack	1	39	6	stand	otherfurniture	Furniture					n03704549	magazine_rack.n.01	shelving	31
+1008	sticker	sticker	1	40	7	sticker	otherprop	Objects					n07272545	gummed_label.n.01	objects	39
+225	lotion	lotion	1	40	7		otherprop	Objects					n03690938	lotion.n.01	objects	39
+1083	buddha	buddha	1	40	7		otherprop	Objects							objects	39
+813	file organizer	file organizer	1	40	7		otherprop	Objects							objects	39
+138	paper towel rolls	paper towel roll	1	40	7	paper towel	otherprop	Objects					n03887697	paper_towel.n.01	towel	20
+1145	night lamp	night lamp	1	35	7	lamp	lamp	Objects	lamp		lamp	3636649	n03636649	lamp.n.02	lighting	28
+796	fuse box	fuse box	1	40	7		otherprop	Objects							misc	40
+1325	knife block	knife block	1	40	7		otherprop	Objects							objects	39
+363	furnace	furnace	1	39	6	furnace	otherfurniture	Furniture					n03404449	furnace.n.01		
+1174	cd cases	cd case	1	40	7		otherprop	Objects							objects	39
+38	stools	stool	1	40	7	stool	otherprop	Objects	stool				n04326896	stool.n.01	stool	19
+1326	hand sanitzer dispenser	hand sanitzer dispenser	1	40	7		otherprop	Objects					n04254120	soap_dispenser.n.01	objects	39
+997	teapot	teapot	1	40	7	tea pot	otherprop	Objects					n04398044	teapot.n.01	objects	39
+1327	pen holder	pen holder	1	40	7		otherprop	Objects							objects	39
+1328	tray rack	tray rack	1	40	7		otherprop	Objects							objects	39
+1329	wig	wig	1	40	7		otherprop	Objects					n04584207	wig.n.01	objects	39
+182	switch	switch	1	40	7		otherprop	Objects					n04372370	switch.n.01	misc	40
+280	plastic containers	plastic container	1	40	7	container	otherprop	Objects					n03094503	container.n.01	objects	39
+1330	night light	night light	1	40	7		otherprop	Objects							lighting	28
+1331	notepad	notepad	1	40	7		otherprop	Objects							objects	39
+1332	mail bin	mail bin	1	40	7		otherprop	Objects							misc	40
+1333	elevator button	elevator button	1	40	7		otherprop	Objects							misc	40
+939	gaming wheel	gaming wheel	1	40	7		otherprop	Objects							objects	39
+1334	drum set	drum set	1	40	7		otherprop	Objects							objects	39
+480	cosmetic bag	cosmetic bag	1	37	7	bag	bag	Objects							objects	39
+907	coffee mug	coffee mug	1	40	7	vessel	otherprop	Objects			cup or mug	3797390	n03063599	coffee_mug.n.01	objects	39
+1335	closet shelf	closet shelf	1	15	6	shelves	shelves	Furniture	bookshelf		bookshelf	2871439	n02871439	bookshelf.n.01	shelving	31
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code/pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt

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code/pointcept/datasets/preprocessing/scannet/preprocess_scannet.py

@@ -0,0 +1,247 @@
+"""
+Preprocessing Script for ScanNet 20/200
+
+Author: Xiaoyang Wu ([email protected])
+Please cite our work if the code is helpful to you.
+"""
+
+import warnings
+
+import torch
+
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+import os
+import argparse
+import glob
+import json
+import plyfile
+import numpy as np
+import pandas as pd
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor
+from itertools import repeat
+
+# Load external constants
+from meta_data.scannet200_constants import VALID_CLASS_IDS_200, VALID_CLASS_IDS_20
+
+CLOUD_FILE_PFIX = "_vh_clean_2"
+SEGMENTS_FILE_PFIX = ".0.010000.segs.json"
+AGGREGATIONS_FILE_PFIX = ".aggregation.json"
+CLASS_IDS200 = VALID_CLASS_IDS_200
+CLASS_IDS20 = VALID_CLASS_IDS_20
+IGNORE_INDEX = -1
+
+
+def read_plymesh(filepath):
+    """Read ply file and return it as numpy array. Returns None if emtpy."""
+    with open(filepath, "rb") as f:
+        plydata = plyfile.PlyData.read(f)
+    if plydata.elements:
+        vertices = pd.DataFrame(plydata["vertex"].data).values
+        faces = np.stack(plydata["face"].data["vertex_indices"], axis=0)
+        return vertices, faces
+
+
+# Map the raw category id to the point cloud
+def point_indices_from_group(seg_indices, group, labels_pd):
+    group_segments = np.array(group["segments"])
+    label = group["label"]
+
+    # Map the category name to id
+    label_id20 = labels_pd[labels_pd["raw_category"] == label]["nyu40id"]
+    label_id20 = int(label_id20.iloc[0]) if len(label_id20) > 0 else 0
+    label_id200 = labels_pd[labels_pd["raw_category"] == label]["id"]
+    label_id200 = int(label_id200.iloc[0]) if len(label_id200) > 0 else 0
+
+    # Only store for the valid categories
+    if label_id20 in CLASS_IDS20:
+        label_id20 = CLASS_IDS20.index(label_id20)
+    else:
+        label_id20 = IGNORE_INDEX
+
+    if label_id200 in CLASS_IDS200:
+        label_id200 = CLASS_IDS200.index(label_id200)
+    else:
+        label_id200 = IGNORE_INDEX
+
+    # get points, where segment indices (points labelled with segment ids) are in the group segment list
+    point_idx = np.where(np.isin(seg_indices, group_segments))[0]
+    return point_idx, label_id20, label_id200
+
+
+def face_normal(vertex, face):
+    v01 = vertex[face[:, 1]] - vertex[face[:, 0]]
+    v02 = vertex[face[:, 2]] - vertex[face[:, 0]]
+    vec = np.cross(v01, v02)
+    length = np.sqrt(np.sum(vec**2, axis=1, keepdims=True)) + 1.0e-8
+    nf = vec / length
+    area = length * 0.5
+    return nf, area
+
+
+def vertex_normal(vertex, face):
+    nf, area = face_normal(vertex, face)
+    nf = nf * area
+
+    nv = np.zeros_like(vertex)
+    for i in range(face.shape[0]):
+        nv[face[i]] += nf[i]
+
+    length = np.sqrt(np.sum(nv**2, axis=1, keepdims=True)) + 1.0e-8
+    nv = nv / length
+    return nv
+
+
+def handle_process(
+    scene_path, output_path, labels_pd, train_scenes, val_scenes, parse_normals=True
+):
+    scene_id = os.path.basename(scene_path)
+    mesh_path = os.path.join(scene_path, f"{scene_id}{CLOUD_FILE_PFIX}.ply")
+    segments_file = os.path.join(
+        scene_path, f"{scene_id}{CLOUD_FILE_PFIX}{SEGMENTS_FILE_PFIX}"
+    )
+    aggregations_file = os.path.join(scene_path, f"{scene_id}{AGGREGATIONS_FILE_PFIX}")
+    info_file = os.path.join(scene_path, f"{scene_id}.txt")
+
+    if scene_id in train_scenes:
+        output_file = os.path.join(output_path, "train", f"{scene_id}.pth")
+        split_name = "train"
+    elif scene_id in val_scenes:
+        output_file = os.path.join(output_path, "val", f"{scene_id}.pth")
+        split_name = "val"
+    else:
+        output_file = os.path.join(output_path, "test", f"{scene_id}.pth")
+        split_name = "test"
+
+    print(f"Processing: {scene_id} in {split_name}")
+
+    vertices, faces = read_plymesh(mesh_path)
+    coords = vertices[:, :3]
+    colors = vertices[:, 3:6]
+    save_dict = dict(coord=coords, color=colors, scene_id=scene_id)
+
+    # # Rotating the mesh to axis aligned
+    # info_dict = {}
+    # with open(info_file) as f:
+    #     for line in f:
+    #         (key, val) = line.split(" = ")
+    #         info_dict[key] = np.fromstring(val, sep=' ')
+    #
+    # if 'axisAlignment' not in info_dict:
+    #     rot_matrix = np.identity(4)
+    # else:
+    #     rot_matrix = info_dict['axisAlignment'].reshape(4, 4)
+    # r_coords = coords.transpose()
+    # r_coords = np.append(r_coords, np.ones((1, r_coords.shape[1])), axis=0)
+    # r_coords = np.dot(rot_matrix, r_coords)
+    # coords = r_coords
+
+    # Parse Normals
+    if parse_normals:
+        save_dict["normal"] = vertex_normal(coords, faces)
+
+    # Load segments file
+    if split_name != "test":
+        with open(segments_file) as f:
+            segments = json.load(f)
+            seg_indices = np.array(segments["segIndices"])
+
+        # Load Aggregations file
+        with open(aggregations_file) as f:
+            aggregation = json.load(f)
+            seg_groups = np.array(aggregation["segGroups"])
+
+        # Generate new labels
+        semantic_gt20 = np.ones((vertices.shape[0])) * IGNORE_INDEX
+        semantic_gt200 = np.ones((vertices.shape[0])) * IGNORE_INDEX
+        instance_ids = np.ones((vertices.shape[0])) * IGNORE_INDEX
+        for group in seg_groups:
+            point_idx, label_id20, label_id200 = point_indices_from_group(
+                seg_indices, group, labels_pd
+            )
+
+            semantic_gt20[point_idx] = label_id20
+            semantic_gt200[point_idx] = label_id200
+            instance_ids[point_idx] = group["id"]
+
+        semantic_gt20 = semantic_gt20.astype(int)
+        semantic_gt200 = semantic_gt200.astype(int)
+        instance_ids = instance_ids.astype(int)
+
+        save_dict["semantic_gt20"] = semantic_gt20
+        save_dict["semantic_gt200"] = semantic_gt200
+        save_dict["instance_gt"] = instance_ids
+
+        # Concatenate with original cloud
+        processed_vertices = np.hstack((semantic_gt200, instance_ids))
+
+        if np.any(np.isnan(processed_vertices)) or not np.all(
+            np.isfinite(processed_vertices)
+        ):
+            raise ValueError(f"Find NaN in Scene: {scene_id}")
+
+    # Save processed data
+    torch.save(save_dict, output_file)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dataset_root",
+        required=True,
+        help="Path to the ScanNet dataset containing scene folders",
+    )
+    parser.add_argument(
+        "--output_root",
+        required=True,
+        help="Output path where train/val folders will be located",
+    )
+    parser.add_argument(
+        "--parse_normals", default=True, type=bool, help="Whether parse point normals"
+    )
+    config = parser.parse_args()
+
+    # Load label map
+    labels_pd = pd.read_csv(
+        "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2-labels.combined.tsv",
+        sep="\t",
+        header=0,
+    )
+
+    # Load train/val splits
+    with open(
+        "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_train.txt"
+    ) as train_file:
+        train_scenes = train_file.read().splitlines()
+    with open(
+        "pointcept/datasets/preprocessing/scannet/meta_data/scannetv2_val.txt"
+    ) as val_file:
+        val_scenes = val_file.read().splitlines()
+
+    # Create output directories
+    train_output_dir = os.path.join(config.output_root, "train")
+    os.makedirs(train_output_dir, exist_ok=True)
+    val_output_dir = os.path.join(config.output_root, "val")
+    os.makedirs(val_output_dir, exist_ok=True)
+    test_output_dir = os.path.join(config.output_root, "test")
+    os.makedirs(test_output_dir, exist_ok=True)
+
+    # Load scene paths
+    scene_paths = sorted(glob.glob(config.dataset_root + "/scans*/scene*"))
+
+    # Preprocess data.
+    print("Processing scenes...")
+    pool = ProcessPoolExecutor(max_workers=mp.cpu_count())
+    # pool = ProcessPoolExecutor(max_workers=1)
+    _ = list(
+        pool.map(
+            handle_process,
+            scene_paths,
+            repeat(config.output_root),
+            repeat(labels_pd),
+            repeat(train_scenes),
+            repeat(val_scenes),
+            repeat(config.parse_normals),
+        )
+    )

code/pointcept/datasets/preprocessing/scannet/scannet_pair/SensorData.py

@@ -0,0 +1,183 @@
+import os, struct
+import numpy as np
+import zlib
+import imageio
+import cv2
+
+COMPRESSION_TYPE_COLOR = {-1: "unknown", 0: "raw", 1: "png", 2: "jpeg"}
+COMPRESSION_TYPE_DEPTH = {
+    -1: "unknown",
+    0: "raw_ushort",
+    1: "zlib_ushort",
+    2: "occi_ushort",
+}
+
+
+class RGBDFrame:
+    def load(self, file_handle):
+        self.camera_to_world = np.asarray(
+            struct.unpack("f" * 16, file_handle.read(16 * 4)), dtype=np.float32
+        ).reshape(4, 4)
+        self.timestamp_color = struct.unpack("Q", file_handle.read(8))[0]
+        self.timestamp_depth = struct.unpack("Q", file_handle.read(8))[0]
+        self.color_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+        self.depth_size_bytes = struct.unpack("Q", file_handle.read(8))[0]
+        self.color_data = b"".join(
+            struct.unpack(
+                "c" * self.color_size_bytes, file_handle.read(self.color_size_bytes)
+            )
+        )
+        self.depth_data = b"".join(
+            struct.unpack(
+                "c" * self.depth_size_bytes, file_handle.read(self.depth_size_bytes)
+            )
+        )
+
+    def decompress_depth(self, compression_type):
+        if compression_type == "zlib_ushort":
+            return self.decompress_depth_zlib()
+        else:
+            raise
+
+    def decompress_depth_zlib(self):
+        return zlib.decompress(self.depth_data)
+
+    def decompress_color(self, compression_type):
+        if compression_type == "jpeg":
+            return self.decompress_color_jpeg()
+        else:
+            raise
+
+    def decompress_color_jpeg(self):
+        return imageio.imread(self.color_data)
+
+
+class SensorData:
+    def __init__(self, filename):
+        self.version = 4
+        self.load(filename)
+
+    def load(self, filename):
+        with open(filename, "rb") as f:
+            version = struct.unpack("I", f.read(4))[0]
+            assert self.version == version
+            strlen = struct.unpack("Q", f.read(8))[0]
+            self.sensor_name = b"".join(struct.unpack("c" * strlen, f.read(strlen)))
+            self.intrinsic_color = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.extrinsic_color = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.intrinsic_depth = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.extrinsic_depth = np.asarray(
+                struct.unpack("f" * 16, f.read(16 * 4)), dtype=np.float32
+            ).reshape(4, 4)
+            self.color_compression_type = COMPRESSION_TYPE_COLOR[
+                struct.unpack("i", f.read(4))[0]
+            ]
+            self.depth_compression_type = COMPRESSION_TYPE_DEPTH[
+                struct.unpack("i", f.read(4))[0]
+            ]
+            self.color_width = struct.unpack("I", f.read(4))[0]
+            self.color_height = struct.unpack("I", f.read(4))[0]
+            self.depth_width = struct.unpack("I", f.read(4))[0]
+            self.depth_height = struct.unpack("I", f.read(4))[0]
+            self.depth_shift = struct.unpack("f", f.read(4))[0]
+            num_frames = struct.unpack("Q", f.read(8))[0]
+            self.frames = []
+            for i in range(num_frames):
+                frame = RGBDFrame()
+                frame.load(f)
+                self.frames.append(frame)
+
+    def export_depth_images(self, output_path, image_size=None, frame_skip=1):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print(
+            "exporting", len(self.frames) // frame_skip, " depth frames to", output_path
+        )
+        for f in range(0, len(self.frames), frame_skip):
+            if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+                continue
+            if f % 100 == 0:
+                print(
+                    "exporting",
+                    f,
+                    "th depth frames to",
+                    os.path.join(output_path, str(f) + ".png"),
+                )
+
+            depth_data = self.frames[f].decompress_depth(self.depth_compression_type)
+            depth = np.fromstring(depth_data, dtype=np.uint16).reshape(
+                self.depth_height, self.depth_width
+            )
+            if image_size is not None:
+                depth = cv2.resize(
+                    depth,
+                    (image_size[1], image_size[0]),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+            imageio.imwrite(os.path.join(output_path, str(f) + ".png"), depth)
+
+    def export_color_images(self, output_path, image_size=None, frame_skip=1):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print(
+            "exporting", len(self.frames) // frame_skip, "color frames to", output_path
+        )
+        for f in range(0, len(self.frames), frame_skip):
+            if os.path.exists((os.path.join(output_path, str(f) + ".png"))):
+                continue
+            if f % 100 == 0:
+                print(
+                    "exporting",
+                    f,
+                    "th color frames to",
+                    os.path.join(output_path, str(f) + ".png"),
+                )
+            color = self.frames[f].decompress_color(self.color_compression_type)
+            if image_size is not None:
+                color = cv2.resize(
+                    color,
+                    (image_size[1], image_size[0]),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+            # imageio.imwrite(os.path.join(output_path, str(f) + '.jpg'), color)
+            imageio.imwrite(os.path.join(output_path, str(f) + ".png"), color)
+
+    def save_mat_to_file(self, matrix, filename):
+        with open(filename, "w") as f:
+            for line in matrix:
+                np.savetxt(f, line[np.newaxis], fmt="%f")
+
+    def export_poses(self, output_path, frame_skip=1):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print(
+            "exporting", len(self.frames) // frame_skip, "camera poses to", output_path
+        )
+        for f in range(0, len(self.frames), frame_skip):
+            self.save_mat_to_file(
+                self.frames[f].camera_to_world,
+                os.path.join(output_path, str(f) + ".txt"),
+            )
+
+    def export_intrinsics(self, output_path):
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        print("exporting camera intrinsics to", output_path)
+        self.save_mat_to_file(
+            self.intrinsic_color, os.path.join(output_path, "intrinsic_color.txt")
+        )
+        self.save_mat_to_file(
+            self.extrinsic_color, os.path.join(output_path, "extrinsic_color.txt")
+        )
+        self.save_mat_to_file(
+            self.intrinsic_depth, os.path.join(output_path, "intrinsic_depth.txt")
+        )
+        self.save_mat_to_file(
+            self.extrinsic_depth, os.path.join(output_path, "extrinsic_depth.txt")
+        )

code/pointcept/datasets/preprocessing/scannet/scannet_pair/compute_full_overlapping.py

@@ -0,0 +1,91 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+import torch
+import numpy as np
+import math
+import glob, os
+import argparse
+import open3d as o3d
+
+
+def make_open3d_point_cloud(xyz, color=None, voxel_size=None):
+    if np.isnan(xyz).any():
+        return None
+
+    xyz = xyz[:, :3]
+    pcd = o3d.geometry.PointCloud()
+    pcd.points = o3d.utility.Vector3dVector(xyz)
+    if color is not None:
+        pcd.colors = o3d.utility.Vector3dVector(color)
+    if voxel_size is not None:
+        pcd = pcd.voxel_down_sample(voxel_size)
+
+    return pcd
+
+
+def compute_overlap_ratio(pcd0, pcd1, voxel_size):
+    pcd0_down = pcd0.voxel_down_sample(voxel_size)
+    pcd1_down = pcd1.voxel_down_sample(voxel_size)
+    matching01 = get_matching_indices(pcd0_down, pcd1_down, voxel_size * 1.5, 1)
+    matching10 = get_matching_indices(pcd1_down, pcd0_down, voxel_size * 1.5, 1)
+    overlap0 = float(len(matching01)) / float(len(pcd0_down.points))
+    overlap1 = float(len(matching10)) / float(len(pcd1_down.points))
+    return max(overlap0, overlap1)
+
+
+def get_matching_indices(source, pcd_tree, search_voxel_size, K=None):
+    match_inds = []
+    for i, point in enumerate(source.points):
+        [_, idx, _] = pcd_tree.search_radius_vector_3d(point, search_voxel_size)
+        if K is not None:
+            idx = idx[:K]
+        for j in idx:
+            match_inds.append((i, j))
+    return match_inds
+
+
+def compute_full_overlapping(data_root, scene_id, voxel_size=0.05):
+    _points = [
+        (
+            pcd_name,
+            make_open3d_point_cloud(
+                torch.load(pcd_name)["coord"], voxel_size=voxel_size
+            ),
+        )
+        for pcd_name in glob.glob(os.path.join(data_root, scene_id, "pcd", "*.pth"))
+    ]
+    points = [(pcd_name, pcd) for (pcd_name, pcd) in _points if pcd is not None]
+    print(
+        "load {} point clouds ({} invalid has been filtered), computing matching/overlapping".format(
+            len(points), len(_points) - len(points)
+        )
+    )
+
+    matching_matrix = np.zeros((len(points), len(points)))
+    for i, (pcd0_name, pcd0) in enumerate(points):
+        print("matching to...{}".format(pcd0_name))
+        pcd0_tree = o3d.geometry.KDTreeFlann(copy.deepcopy(pcd0))
+        for j, (pcd1_name, pcd1) in enumerate(points):
+            if i == j:
+                continue
+            matching_matrix[i, j] = float(
+                len(get_matching_indices(pcd1, pcd0_tree, 1.5 * voxel_size, 1))
+            ) / float(len(pcd1.points))
+
+    # write to file
+    with open(os.path.join(data_root, scene_id, "pcd", "overlap.txt"), "w") as f:
+        for i, (pcd0_name, pcd0) in enumerate(points):
+            for j, (pcd1_name, pcd1) in enumerate(points):
+                if i < j:
+                    overlap = max(matching_matrix[i, j], matching_matrix[j, i])
+                    f.write(
+                        "{} {} {}\n".format(
+                            pcd0_name.replace(data_root, ""),
+                            pcd1_name.replace(data_root, ""),
+                            overlap,
+                        )
+                    )

code/pointcept/datasets/preprocessing/scannet/scannet_pair/generage_list.py

@@ -0,0 +1,33 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import argparse
+import glob, os, sys
+
+from SensorData import SensorData
+
+# params
+parser = argparse.ArgumentParser()
+# data paths
+parser.add_argument("--target_dir", required=True, help="path to the target dir")
+
+opt = parser.parse_args()
+print(opt)
+
+
+def main():
+    overlaps = glob.glob(os.path.join(opt.target_dir, "*/pcd/overlap.txt"))
+    with open(os.path.join(opt.target_dir, "overlap30.txt"), "w") as f:
+        for fo in overlaps:
+            for line in open(fo):
+                pcd0, pcd1, op = line.strip().split()
+                if float(op) >= 0.3:
+                    print("{} {} {}".format(pcd0, pcd1, op), file=f)
+    print("done")
+
+
+if __name__ == "__main__":
+    main()