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Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation

This is an official implementation of the following paper:

  • Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation
    Zhonghua Wu*, Yicheng Wu*, Guosheng Lin, Jianfei Cai
    International Journal of Computer Vision (IJCV) 2024
    [ Arxiv ]

News

  • Jan, 2024: Initial release our RAC-Net codebase.

Overview

Installation

Requirements

  • Ubuntu: 18.04 or higher
  • CUDA: 10.2 or higher
  • PyTorch: 1.10.0 ~ 1.11.0

Conda Environment

conda create -n pcr python=3.8 -y
conda activate pcr
conda install ninja -y
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge -y
conda install -c anaconda h5py pyyaml -y
conda install -c conda-forge sharedarray tensorboardx yapf addict einops scipy plyfile termcolor timm -y
conda install -c pyg pytorch-cluster pytorch-scatter pytorch-sparse -y
pip install torch-geometric

# spconv (SparseUNet)
# refer https://github.com/traveller59/spconv
pip install spconv-cu113

# Open3D (Visualization)
pip install open3d

# PTv1 & PTv2
cd libs/pointops
python setup.py install
cd ../..

# stratified transformer
pip install torch-points3d

# fix dependence, caused by install torch-points3d 
pip uninstall SharedArray
pip install SharedArray==3.2.1

cd libs/pointops2
python setup.py install
cd ../..

# MinkowskiEngine (SparseUNet)
# refer https://github.com/NVIDIA/MinkowskiEngine

# torchsparse (SPVCNN)
# refer https://github.com/mit-han-lab/torchsparse
# install method without sudo apt install
conda install google-sparsehash -c bioconda
export C_INCLUDE_PATH=${CONDA_PREFIX}/include:$C_INCLUDE_PATH
export CPLUS_INCLUDE_PATH=${CONDA_PREFIX}/include:CPLUS_INCLUDE_PATH
pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git

Data Preparation

ScanNet v2

The preprocessing support semantic and instance segmentation for both ScanNet20 and ScanNet200.

  • Download the ScanNet v2 dataset.
  • Download ScanNet 20 points annotations to "./pcr/datasets/preprocessing/scannet/".
  • Run preprocessing code for raw ScanNet as follows:
# RAW_SCANNET_DIR: the directory of downloaded ScanNet v2 raw dataset.
# PROCESSED_SCANNET_DIR: the directory of processed ScanNet dataset (output dir).
python pcr/datasets/preprocessing/scannet/preprocess_scannet.py --dataset_root ${RAW_SCANNET_DIR} --output_root ${PROCESSED_SCANNET_DIR}
  • Link processed dataset to codebase:
# PROCESSED_SCANNET_DIR: the directory of processed ScanNet dataset.
mkdir data
ln -s ${RAW_SCANNET_DIR} ${CODEBASE_DIR}/data/scannet

Quick Start

Training

Train from scratch. The training processing is based on configs in configs folder. The training script will generate an experiment folder in exp folder and backup essential code in the experiment folder. Training config, log, tensorboard and checkpoints will also be saved into the experiment folder during the training process.

export CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES}
# Script (Recommended)
sh scripts/train.sh -p ${INTERPRETER_PATH} -g ${NUM_GPU} -d ${DATASET_NAME} -c ${CONFIG_NAME} -n ${EXP_NAME}
# Direct
export PYTHONPATH=./
python tools/train.py --config-file ${CONFIG_PATH} --num-gpus ${NUM_GPU} --options save_path=${SAVE_PATH}

For example:

# By script (Recommended)
# -p is default set as python and can be ignored
sh scripts/train.sh -p python -d scannet -c semseg-ptv1-0-base -n semseg-ptv1-0-base
# Direct
export PYTHONPATH=./
python tools/train.py --config-file configs/scannet/semseg-ptv1-0-base.py --options save_path=exp/scannet/semseg-ptv1-0-base

Testing

The validation during training only evaluate model on point clouds after grid sampling (voxelization) and testing is need to achieve a precise evaluate result. Our testing code support TTA (test time augmentation) testing. (Currently only support testing on a single GPU, I might add support to multi-gpus testing in the future version.)

# By script (Based on experiment folder created by training script)
sh scripts/test.sh -p ${INTERPRETER_PATH} -d ${DATASET_NAME} -n ${EXP_NAME} -w ${CHECKPOINT_NAME}
# Direct
export PYTHONPATH=./
python tools/test.py --config-file ${CONFIG_PATH} --options save_path=${SAVE_PATH} weight=${CHECKPOINT_PATH}

For example:

# By script (Based on experiment folder created by training script)
# -p is default set as python and can be ignored
# -w is default set as model_best and can be ignored
sh scripts/test.sh -p python -d scannet -n semseg-ptv1-0-base -w model_best
# Direct
export PYTHONPATH=./
python tools/test.py --config-file configs/scannet/semseg-ptv1-0-base.py --options save_path=exp/scannet/semseg-ptv1-0-base weight=exp/scannet/semseg-ptv1-0-base/models/model_best.pth

Model Zoo

Coming soon...

Citation

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

@article{wu2024reliability,
  title={Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation},
  author={Wu, Zhonghua and Wu, Yicheng and Lin, Guosheng and Cai, Jianfei},
  journal={International Journal of Computer Vision},
  pages={1--14},
  year={2024},
  publisher={Springer}
}

Acknowledgement

The repo is derived from Point Transformer code and inspirited by several repos, e.g., PointTransformer, MinkowskiEngine, pointnet2, mmcv, and Detectron2.