NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations

NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations
Joy Hsu, Jiayuan Mao, Jiajun Wu
In Conference on Computer Vision and Pattern Recognition (CVPR) 2023

Dataset

Our dataset download process follows the ReferIt3D benchmark.

Specifically, you will need to

(1) Download sr3d_train.csv and sr3d_test.csv from this link
(2) Download scans from ScanNet and process them according to this link. This should result in a keep_all_points_with_global_scan_alignment.pkl file.

Setup

Run the following commands to install necessary dependencies.

  conda create -n ns3d python=3.7.11
  conda activate ns3d
  pip install -r requirements.txt

Install Jacinle.

  git clone https://github.com/vacancy/Jacinle --recursive
  export PATH=<path_to_jacinle>/bin:$PATH

Install the referit3d python package from ReferIt3D.

  git clone https://github.com/referit3d/referit3d
  cd referit3d
  pip install -e .

Compile CUDA layers for PointNet++.

  cd models/scene_graph/point_net_pp/pointnet2
  python setup.py install

Evaluation

To evaluate NS3D:

  scannet=<path_to/keep_all_points_with_global_scan_alignment.pkl>
  referit=<path_to/sr3d_train.csv>
  load_path=<path_to/model_to_evaluate.pth>
  
  jac-run ns3d/trainval.py --desc ns3d/desc_ns3d.py --scannet-file $scannet --referit3D-file $referit --load $load_path --evaluate

Weights for our trained NS3D model can be found at trained_ns3d.pth and loaded into load_path.

Training

To train NS3D:

  scannet=<path_to/keep_all_points_with_global_scan_alignment.pkl>
  referit=<path_to/sr3d_train.csv>
  load_path=<path_to/pretrained_classification_model.pth>
  
  jac-run ns3d/trainval.py --desc ns3d/desc_ns3d.py --scannet-file $scannet --referit3D-file $referit --load $load_path --lr 0.0001 --epochs 5000 --save-interval 1 --validation-interval 1

Weights for our pretrained classification model can be found at pretrained_cls.pth and loaded into load_path.

Acknowledgements