BSNet

This is the official PyTorch implementation of BSNet (BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (CVPR2024)).

BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation (CVPR2024) [Paper]

Jiahao Lu, Jiacheng Deng, Tianzhu Zhang

Get Started

Environment

Requirements

Python 3.x
Pytorch 1.10
CUDA 10.x or higher

The following installation suppose python=3.8 pytorch=1.10 and cuda=11.4.

Create a conda virtual environment

conda create -n BSNet python=3.8
conda activate BSNet

Install the dependencies

Install Pytorch 1.10
```
pip install spconv-cu114
conda install pytorch-scatter -c pyg
pip install -r requirements.txt
```
Install segmentator from this repo (We wrap the segmentator in ScanNet).

Setup, Install spformer and pointgroup_ops.

sudo apt-get install libsparsehash-dev
python setup.py develop
cd spformer/lib/
python setup.py develop

Data Preparation

ScanNet v2 dataset

Download the ScanNet v2 dataset.

Put the downloaded scans and scans_test folder as follows.

MMImp
├── data
│   ├── scannetv2
│   │   ├── scans
│   │   ├── scans_test

Split and preprocess data

cd data/scannetv2
bash prepare_data.sh

The script data into train/val/test folder and preprocess the data. After running the script the scannet dataset structure should look like below.

BSNet
├── data
│   ├── scannetv2
│   │   ├── scans
│   │   ├── scans_test
│   │   ├── train
│   │   ├── val
│   │   ├── test
│   │   ├── val_gt

Generate Superpoints

Codes come from: https://github.com/ScanNet/ScanNet/tree/master/Segmentator.

python prepare_superpoint.py

Generate Pseudo Labels (Coming Soon!)

Here, we provide the pseudo labels generated by our method in Google Drive.

Generate No-overlapping Objects Google Drive
Train Simulated Samples
Train Mean Teacher
Generate Pseudo Labels

Training

The training steps are the same as the corresponding origin repositories. More details can be referred to SPFormer, ISBNet, Maft. Please note that the pretrained weights of SPFormer and Maft are identical to Gapro's.

Pre-trained Models

dataset	Model	AP	AP_50%	AP_25%	Download
[ScanNetv2]	SPFormer	56.3	73.9	-	Model Weight
[ScanNetv2]	ISBNet	54.5	73.1	82.5	Model Weight
[ScanNetv2]	MAFT	58.4	75.9	-	Model Weight
[ScanNetv2]	SPFormer + Ours	53.3	72.7	83.4	Model Weight
[ScanNetv2]	ISBNet + Ours	52.8	71.6	82.6	Model Weight
[ScanNetv2]	MAFT + Ours	56.2	75.9	85.7	Model Weight

Acknowledgements

This repo is built upon ISBNet,SPFormer, Maft, Gapro.

Citation

If you find this project useful, please consider citing:

@inproceedings{lu2024bsnet,
  title={BSNet: Box-Supervised Simulation-assisted Mean Teacher for 3D Instance Segmentation},
  author={Lu, Jiahao and Deng, Jiacheng and Zhang, Tianzhu},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20374--20384},
  year={2024}
}

peoplelu/BSNet