readme: ---
  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. 
  