This is the official implementation of 3DAC, a learning-based data compression framework for point cloud attributes. For technical details, please refer to:

3DAC: Learning Attribute Compression for Point Clouds
Guangchi Fang, Qingyong Hu, Hanyun Wang, Yiling Xu, Yulan Guo.
[Paper]

This code has been tested with Python 3.7, torch 1.7.0, CUDA 10.2.

• Clone the repository

git clone [email protected]:fatPeter/ThreeDAC.git && cd ThreeDAC

• Setup python environment

conda create -n ThreeDAC python=3.7
conda activate ThreeDAC
pip install -r helper_requirements.txt

• Install torchsparse

sudo apt-get install libsparsehash-dev
pip install git+https://github.com/mit-han-lab/[email protected]

1. Download the ScanNet dataset from the official website and move it to ./dataset.
2. Voxelize the point cloud geometry with a 9-depth octree and deduplicate redundant points. We merge this process to the dataloader to simplify our code.

• Train/eval and compress/decompress scripts are in ./ScanNet:

cd ScanNet

• Train on ScanNet (Qstep denotes the quantization step):

python train.py --Qstep=10

• Quick eval:

python eval.py --Qstep=10

• Compress scans of ScanNet:

python compress.py --Qstep=10

• Decompress:

python decompress.py --Qstep=10

1. SemanticKITTI dataset can be found here. Download the files and extract everything into the same folder. Uncompress the folder and move it to ./dataset.
2. Voxelize the point cloud geometry with a 12-depth octree and deduplicate redundant points. We merge this process to the dataloader to simplify our code.

• Train/eval and compress/decompress scripts are in ./SemanticKITTI:

cd SemanticKITTI

• Train on SemanticKITTI (step: use part of data for quick testing):

python train.py --Qstep=10 --step=20

• Compress scans of ScanNet:

python compress.py --Qstep=10 --step=200

• Decompress:

python decompress.py --Qstep=10 --step=200

If you find our work useful in your research, please consider citing:

@article{fang20223dac,
  title={3DAC: Learning Attribute Compression for Point Clouds},
  author={Fang, Guangchi and Hu, Qingyong and Wang, Hanyun and Xu, Yiling and Guo, Yulan},
  journal={arXiv preprint arXiv:2203.09931},
  year={2022}
}