Bridge3D

Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models

This is the official repository for the NeurIPS 2023 paper: Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models.

1. Requirements

PyTorch >= 1.8.0; python >= 3.8;

pip install -r requirements.txt

# Chamfer Distance & emd
cd ./Pretrain/extensions/chamfer_dist
python setup.py install --user
cd ./Pretrain/extensions/emd
python setup.py install --user
# PointNet++
pip install "git+https://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
# GPU kNN
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl

2. Datasets

See the Preprocess parts.

3. Bridge3D Weights

Pre-training here

3detr on Scannet here

4. Bridge3D Pre-training

CUDA_VISIBLE_DEVICES=0,1 python main.py --config cfgs/pretrain/base.yaml --exp_name ./output

5. Fine-tuning

See Downstream parts

Cite

If you find our work helpful for your research. Please consider citing our paper.

@article{chen2023bridging,
  title={Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models},
  author={Chen, Zhimin and Li, Bing},
  journal={arXiv preprint arXiv:2305.08776},
  year={2023}
}