Create README

README.md

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+---
+tags:
+- vision
+- 3D
+- 3D object detection
+datasets:
+- omni3d
+metrics:
+- AP
+---
+
+# 3D Object Detection with Cube R-CNN
+
+3D Object Detection with Cube R-CNN is described in [**Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild**](https://arxiv.org/abs/2207.10660) and released in this [repository](https://github.com/facebookresearch/omni3d)
+
+## Overview
+A description of the model and its architecture are shown below
+
+<img src="https://s3.amazonaws.com/moonup/production/uploads/1666115971617-634ededbd049354d7ee4b557.png" width=700px/>    
+
+## Training Data
+
+Cube R-CNN was trained on Omni3D, a large benchmark for 3D object detection in the wild.
+
+## Demo: Inference on Any Image
+
+The model detects objects in 3D from a single image. There are 50 distinct object categories including *car, truck, chair, table, cabinet, books, and many more*.
+The model assumes known focal length for the image in order to predict the right metric scale. 
+However, users can provide any focal length and will get predictions on a "relative" scale. 
+
+For example, we can predict 3D objects from COCO images with a user-defined focal length of 4.0, as shown below
+
+<img src="https://github.com/facebookresearch/omni3d/blob/main/.github/generalization_coco.png?raw=true" width=500px/>
+
+The above output is produced by our demo
+
+```bash
+python demo/demo.py \
+--config cubercnn://omni3d/cubercnn_DLA34_FPN.yaml \
+--input-folder "datasets/image_inputs" \
+--threshold 0.25 --focal 4.0 --display \
+MODEL.WEIGHTS cubercnn://omni3d/cubercnn_DLA34_FPN.pth \
+OUTPUT_DIR output/demo 
+```
+
+## Checkpoints
+
+You can find model checkpoints in the original [model zoo](https://github.com/facebookresearch/omni3d/blob/main/MODEL_ZOO.md).
+
+## Intended Use and Limitations
+
+Cube R-CNN is a data-driven method trained on an annotated dataset, Omni3D. The purpose of the project is to advance 3D computer vision and 3D object recognition. The dataset contains a *pedestrian* category, which we acknowledge as a potential issue in the case of unethical applications of our model.
+
+The limitations of our approach are: erroneous predictions especially for far away objects, mistakes in predicting rotations and depth. Our evaluation reports an analysis for various depths and object sizes to better understand performance.