Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation

This is the official PyTorch implementation of the papers :

Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation (ACCV 2022) [arxiv]

by Linghua Tang, Le Hui, and Jin Xie.

Installation

1) Requirements

• Python 3.7.0
• Pytorch 1.7.1
• CUDA 10.1
• GPU NVIDIA TITAN RTX

2) Anaconda Virtual Environment

conda create -n 3DWSIS python=3.7
conda activate 3DWSIS

3) Clone the repository.

git clone https://github.com/fpthink/3D-WSIS.git --recursive

4) Install the requirements.

cd 3DWSIS
pip install -r requirements.txt
conda install -c bioconda google-sparsehash 

5) Install spconv and pointgroup_ops

[spconv] [pointgroup_ops]

Please refer to PointGroup to install.

ScanNet v2

Data Preparation

Please refer to the ScanNetV2.md in data/ScanNetV2 to process data.

Training

Please set $ScanNetV2_DATA on Line 29 of config/ScanNet_v2_3D_WSIS.yaml.

CUDA_VISIBLE_DEVICES=0 python train_scannetv2.py --config config/ScanNet_v2_3D_WSIS.yaml

Evaluation

CUDA_VISIBLE_DEVICES=0 python test_scannetv2.py --config config/ScanNet_v2_3D_WSIS.yaml --pretrain log/ScanNet_v2_3D_WSIS/epoch_00120_whole_scene.pth

S3DIS

Data Preparation

Please refer to the S3DIS.md in data/S3DIS to process data.

Training

Please set $S3DIS_DATA/data on Line 29 of config/S3DIS_Area5_3D_WSIS.yaml.

CUDA_VISIBLE_DEVICES=0 python train_s3dis.py --config config/S3DIS_Area5_3D_WSIS.yaml

Evaluation

CUDA_VISIBLE_DEVICES=0 python test_s3dis.py --config config/S3DIS_Area5_3D_WSIS.yaml --pretrain log/S3DIS_Area5_3D_WSIS/epoch_00300_whole_scene.pth

Pretrained Model

ScanNet v2 validation :

[Baidu Cloud] [Google Dirve]

Its performance on ScanNet-v2 validation set is 29.8/48.4/67.7 in terms of mAP/mAP50/mAP25.

S3DIS Area5 :

[Baidu Cloud] [Google Dirve]

Its performance on S3DIS Area5 set is 22.4/35.2/47.2/43.2/44.7/51.8/41.3 in terms of mAP/mAP50/mAP25/mCov/mWCov/mPrec/mRec.

Note : Due to the randomness of weak label generation, the results of network training fluctuate slightly.

Acknowledgement

This repo is built upon several repos, e.g., PointGroup, SSTNet, HAIS, spconv and ScanNet.

TODO

• [X] release S3DIS dataset

Citation

If you find this work useful in your research, please cite:

@inproceedings{tang20223dwsis,
    author    = {Tang, Linghua and Hui, Le and Xie, Jin},
    title     = {Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation},
    booktitle = {ACCV},
    year      = {2022},
}
@inproceedings{hui2022graphcut,
    author    = {Hui, Le and Tang, Linghua and Shen, Yaqi and Xie, Jin and Yang, Jian},
    title     = {Learning Superpoint Graph Cut for 3D Instance Segmentation},
    booktitle = {NeurIPS},
    year      = {2022},
}