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import torch
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import numpy as np
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from plyfile import PlyData, PlyElement
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from .general_utils import inverse_sigmoid, strip_symmetric, build_scaling_rotation
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import utils3d
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class Gaussian:
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def __init__(
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self,
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aabb : list,
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sh_degree : int = 0,
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mininum_kernel_size : float = 0.0,
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scaling_bias : float = 0.01,
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opacity_bias : float = 0.1,
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scaling_activation : str = "exp",
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device='cuda'
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):
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self.init_params = {
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'aabb': aabb,
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'sh_degree': sh_degree,
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'mininum_kernel_size': mininum_kernel_size,
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'scaling_bias': scaling_bias,
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'opacity_bias': opacity_bias,
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'scaling_activation': scaling_activation,
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}
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self.sh_degree = sh_degree
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self.active_sh_degree = sh_degree
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self.mininum_kernel_size = mininum_kernel_size
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self.scaling_bias = scaling_bias
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self.opacity_bias = opacity_bias
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self.scaling_activation_type = scaling_activation
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self.device = device
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self.aabb = torch.tensor(aabb, dtype=torch.float32, device=device)
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self.setup_functions()
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self._xyz = None
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self._features_dc = None
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self._features_rest = None
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self._scaling = None
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self._rotation = None
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self._opacity = None
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def setup_functions(self):
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def build_covariance_from_scaling_rotation(scaling, scaling_modifier, rotation):
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L = build_scaling_rotation(scaling_modifier * scaling, rotation)
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actual_covariance = L @ L.transpose(1, 2)
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symm = strip_symmetric(actual_covariance)
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return symm
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if self.scaling_activation_type == "exp":
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self.scaling_activation = torch.exp
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self.inverse_scaling_activation = torch.log
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elif self.scaling_activation_type == "softplus":
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self.scaling_activation = torch.nn.functional.softplus
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self.inverse_scaling_activation = lambda x: x + torch.log(-torch.expm1(-x))
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self.covariance_activation = build_covariance_from_scaling_rotation
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self.opacity_activation = torch.sigmoid
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self.inverse_opacity_activation = inverse_sigmoid
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self.rotation_activation = torch.nn.functional.normalize
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self.scale_bias = self.inverse_scaling_activation(torch.tensor(self.scaling_bias)).cuda()
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self.rots_bias = torch.zeros((4)).cuda()
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self.rots_bias[0] = 1
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self.opacity_bias = self.inverse_opacity_activation(torch.tensor(self.opacity_bias)).cuda()
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@property
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def get_scaling(self):
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scales = self.scaling_activation(self._scaling + self.scale_bias)
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scales = torch.square(scales) + self.mininum_kernel_size ** 2
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scales = torch.sqrt(scales)
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return scales
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@property
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def get_rotation(self):
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return self.rotation_activation(self._rotation + self.rots_bias[None, :])
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@property
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def get_xyz(self):
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return self._xyz * self.aabb[None, 3:] + self.aabb[None, :3]
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@property
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def get_features(self):
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return torch.cat((self._features_dc, self._features_rest), dim=2) if self._features_rest is not None else self._features_dc
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@property
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def get_opacity(self):
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return self.opacity_activation(self._opacity + self.opacity_bias)
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def get_covariance(self, scaling_modifier = 1):
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return self.covariance_activation(self.get_scaling, scaling_modifier, self._rotation + self.rots_bias[None, :])
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def from_scaling(self, scales):
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scales = torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)
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self._scaling = self.inverse_scaling_activation(scales) - self.scale_bias
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def from_rotation(self, rots):
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self._rotation = rots - self.rots_bias[None, :]
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def from_xyz(self, xyz):
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self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:]
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def from_features(self, features):
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self._features_dc = features
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def from_opacity(self, opacities):
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self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias
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def construct_list_of_attributes(self):
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l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
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for i in range(self._features_dc.shape[1]*self._features_dc.shape[2]):
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l.append('f_dc_{}'.format(i))
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l.append('opacity')
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for i in range(self._scaling.shape[1]):
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l.append('scale_{}'.format(i))
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for i in range(self._rotation.shape[1]):
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l.append('rot_{}'.format(i))
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return l
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def save_ply(self, path, transform=[[1, 0, 0], [0, 0, -1], [0, 1, 0]]):
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xyz = self.get_xyz.detach().cpu().numpy()
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normals = np.zeros_like(xyz)
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f_dc = self._features_dc.detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
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opacities = inverse_sigmoid(self.get_opacity).detach().cpu().numpy()
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scale = torch.log(self.get_scaling).detach().cpu().numpy()
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rotation = (self._rotation + self.rots_bias[None, :]).detach().cpu().numpy()
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if transform is not None:
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transform = np.array(transform)
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xyz = np.matmul(xyz, transform.T)
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rotation = utils3d.numpy.quaternion_to_matrix(rotation)
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rotation = np.matmul(transform, rotation)
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rotation = utils3d.numpy.matrix_to_quaternion(rotation)
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dtype_full = [(attribute, 'f4') for attribute in self.construct_list_of_attributes()]
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elements = np.empty(xyz.shape[0], dtype=dtype_full)
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attributes = np.concatenate((xyz, normals, f_dc, opacities, scale, rotation), axis=1)
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elements[:] = list(map(tuple, attributes))
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el = PlyElement.describe(elements, 'vertex')
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PlyData([el]).write(path)
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def load_ply(self, path, transform=[[1, 0, 0], [0, 0, -1], [0, 1, 0]]):
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plydata = PlyData.read(path)
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xyz = np.stack((np.asarray(plydata.elements[0]["x"]),
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np.asarray(plydata.elements[0]["y"]),
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np.asarray(plydata.elements[0]["z"])), axis=1)
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opacities = np.asarray(plydata.elements[0]["opacity"])[..., np.newaxis]
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features_dc = np.zeros((xyz.shape[0], 3, 1))
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features_dc[:, 0, 0] = np.asarray(plydata.elements[0]["f_dc_0"])
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features_dc[:, 1, 0] = np.asarray(plydata.elements[0]["f_dc_1"])
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features_dc[:, 2, 0] = np.asarray(plydata.elements[0]["f_dc_2"])
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if self.sh_degree > 0:
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extra_f_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("f_rest_")]
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extra_f_names = sorted(extra_f_names, key = lambda x: int(x.split('_')[-1]))
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assert len(extra_f_names)==3*(self.sh_degree + 1) ** 2 - 3
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features_extra = np.zeros((xyz.shape[0], len(extra_f_names)))
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for idx, attr_name in enumerate(extra_f_names):
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features_extra[:, idx] = np.asarray(plydata.elements[0][attr_name])
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features_extra = features_extra.reshape((features_extra.shape[0], 3, (self.max_sh_degree + 1) ** 2 - 1))
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scale_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("scale_")]
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scale_names = sorted(scale_names, key = lambda x: int(x.split('_')[-1]))
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scales = np.zeros((xyz.shape[0], len(scale_names)))
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for idx, attr_name in enumerate(scale_names):
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scales[:, idx] = np.asarray(plydata.elements[0][attr_name])
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rot_names = [p.name for p in plydata.elements[0].properties if p.name.startswith("rot")]
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rot_names = sorted(rot_names, key = lambda x: int(x.split('_')[-1]))
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rots = np.zeros((xyz.shape[0], len(rot_names)))
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for idx, attr_name in enumerate(rot_names):
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rots[:, idx] = np.asarray(plydata.elements[0][attr_name])
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if transform is not None:
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transform = np.array(transform)
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xyz = np.matmul(xyz, transform)
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rotation = utils3d.numpy.quaternion_to_matrix(rotation)
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rotation = np.matmul(rotation, transform)
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rotation = utils3d.numpy.matrix_to_quaternion(rotation)
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xyz = torch.tensor(xyz, dtype=torch.float, device=self.device)
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features_dc = torch.tensor(features_dc, dtype=torch.float, device=self.device).transpose(1, 2).contiguous()
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if self.sh_degree > 0:
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features_extra = torch.tensor(features_extra, dtype=torch.float, device=self.device).transpose(1, 2).contiguous()
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opacities = torch.sigmoid(torch.tensor(opacities, dtype=torch.float, device=self.device))
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scales = torch.exp(torch.tensor(scales, dtype=torch.float, device=self.device))
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rots = torch.tensor(rots, dtype=torch.float, device=self.device)
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self._xyz = (xyz - self.aabb[None, :3]) / self.aabb[None, 3:]
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self._features_dc = features_dc
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if self.sh_degree > 0:
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self._features_rest = features_extra
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else:
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self._features_rest = None
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self._opacity = self.inverse_opacity_activation(opacities) - self.opacity_bias
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self._scaling = self.inverse_scaling_activation(torch.sqrt(torch.square(scales) - self.mininum_kernel_size ** 2)) - self.scale_bias
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self._rotation = rots - self.rots_bias[None, :]
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