Awesome

CVPR 2023

MarS3D is a plug-and-play motion-aware module for semantic segmentation on multi-scan 3D point clouds. Extensive experiments show that MarS3D can improve the performance of the baseline model by a large margin.

video | arXiv | paper

Interesting Features

• A plug-and-play module, which can be flexibly integrated with mainstream single-scan segmentation models.
• A Cross-Frame Feature Embedding for temporal information preservation.
• A BEV-based Motion-Aware Feature Learning module to exploit temporal information and enhance the model’s motion awareness.

Performance

• Performance improvement over baselines on SemanticKITTI public multi-scan validation set.

  Method	mIoU	#param
  SPVCNN	49.70%	21.8M
  SPVCNN+MarS3D	54.66%	21.9M
  SparseUNet	48.99%	39.2M
  SparseUNet+MarS3D	54.64%	39.3M
  MinkUNet	48.47%	37.9M
  MinkUNet+MarS3D	54.71%	38.0M

• Comparison with the state-of-the-art models on SemanticKITTI multi-scan benchmark.

  Method	mIoU
  SpSequenceNet	43.1%
  TemporalLidarSeg	47.0%
  TemporalLatticeNet	47.1%
  Meta-RangeSeg	49.5%
  KPConv	51.2%
  SPVCNN	49.2%
  SPVCNN+MarS3D	52.7%

Getting Started

Installatioon

System requirements

• Ubuntu: 18.04+
• CUDA: 11.x

Python dependencies

conda create -n mars3d python=3.9 -y
conda activate mars3d
conda install ninja -y
conda install pytorch torchvision torchaudio cudatoolkit -c pytorch -y  # follow the instructions from the official pytorch website
conda install -c anaconda h5py pyyaml -y
conda install -c conda-forge sharedarray tensorboardx yapf addict einops scipy plyfile -y
pip install tqdm
pip install numpy==1.23 # for fixing version conflict, should < 1.25

Backbone dependencies

# Please follow the official instructions from the official repo for different backbones
# Minkowski Engine: https://github.com/NVIDIA/MinkowskiEngine

# spconv: https://github.com/traveller59/spconv

# torchsparse: https://github.com/mit-han-lab/torchsparse

# torchsparse: an optional installation 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

Dataset Preparation

Semantic-Kitti

• Download the Semantic-Kitti
• The dataset file structure is listed as follows:

./
├── 
├── ...
└── path_to_semantic_kitti/
    ├──sequences
        ├── 00/           
        │   ├── velodyne/	
        |   |	├── 000000.bin
        |   |	├── 000001.bin
        |   |	└── ...
        │   └── labels/ 
        |       ├── 000000.label
        |       ├── 000001.label
        |       └── ...
        ├── 08/ # 08 is the validation set
        ├── 11/ # 11-21 is the test set
        └── ...
        └── 21/

• Symlink the paths to them as follows:

mkdir data
ln -s /path_to_semantic_kitti data/semantic_kitti

Run

Training

sh scripts/train.sh -p INTERPRETER_PATH -d DATASET -c CONFIG_NAME -n EXP_NAME

for example:

sh scripts/train.sh -p python -d semantic_kitti -c spvcnn-mh -n mars3d-spvcnn

Inference

For calling test script, exp folder generated in training process start by training script is needed.

Furthermore, currently, the weight path should be specified in test.py

sh scripts/test.sh -p INTERPRETER_PATH -d DATASET -c CONFIG_NAME -n EXP_NAME

for example:

sh scripts/test.sh -p python -d semantic_kitti -c spvcnn-mh -n mars3d-spvcnn

Citation

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

@inproceedings{liu2023mars3d,
  title={MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds},
  author={Liu, Jiahui and Chang, Chirui and Liu, Jianhui and Wu, Xiaoyang and Ma, Lan and Qi, Xiaojuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9372--9381},
  year={2023}
}