D-PCC

This is the PyTorch implementation of "Density-preserving Deep Point Cloud Compression" (CVPR 2022).

Installation

Install the following packages

python==3.7.12
torch==1.7.1
CUDA==11.0
numpy==1.20.3
open3d==0.9.0.0
einops==0.3.2
scikit-learn==1.0.1
compressai
pickle
argparse

Install the built-in libraries

cd models/Chamfer3D
python setup.py
cd ../pointops
python setup.py

These commands are tested on an ubuntu 16.04 system.

Data Preparation

First download the ShapeNetCore v1 and SemanticKITTI datasets, and then divide them into non-overlapping blocks.

ShapeNet

# install the `Manifold' program
cd ./dataset
git clone https://github.com/hjwdzh/Manifold
cd Manifold && mkdir build
cd build 
cmake .. -DCMAKE_BUILD_TYPE=Release
make 
cd ..

# divide into blocks
python prepare_shapenet.py --date_root path/to/shapenet

SemanticKITTI

python prepare_semantickitti.py --data_root path/to/semantickitti

Please refer to the associated code files for the detailed usages and meanings of other arguments (e.g. cube_size, max_num, etc), and you can adjust them by yourself.

The final file structure is shown as follow:

data  
└───semantickitti
│   │   semantickitti_test_xxx.pkl 
│   │   semantickitti_train_xxx.pkl
│   │   semantickitti_val_xxx.pkl
└───shapenet
│   │   mesh # watertight meshes or cad models
│   │   pcd # sampled point clouds
│   │   shapenet_test_xxx.pkl
│   │   shapenet_train_xxx.pkl
│   │   shapenet_val_xxx.pkl
│   │   test.txt
│   │   train.txt
│   │   val.txt

Train

Position Compression

# shapenet
python train.py --dataset shapenet
# semantickitti
python train.py --dataset semantickitti

Normal Compression

# shapenet
python train.py --dataset shapenet --compress_normal True
# semantickitti
python train.py --dataset semantickitti --compress_normal True

You can manually adjust the bpp_lambda, downsample_rate and quantize_latent_xyzs arguments to achieve different compression ratios, please refer to the ./args/semantickitti_args.py and ./args/shapenet_args.py files for the details, and we also give some examples below. The output files will be saved at ./output/experiment_id/ckpt by default.

# to increase the compression ratio, you can try either of the following commands
python train.py --dataset shapenet --bpp_lambda 1e-2
python train.py --dataset shapenet --downsample_rate 0.25 0.33 0.33
# you can also adjust both bpp_lambda and downsample_rate arguments

# to decrease the compression ratio, you can try any of the following commands
python train.py --dataset shapenet --bpp_lambda 1e-4
python train.py --dataset shapenet --downsample_rate 0.5 0.33 0.33
python train.py --dataset shapenet --quantize_latent_xyzs False
# you can also adjust multi-arguments simultaneously

Test

Position Compression

# shapenet
python test.py --dataset shapenet --model_path path/to/model
# semantickitti
python test.py --dataset semantickitti --model_path path/to/model

Normal Compression

# shapenet
python test.py --dataset shapenet --compress_normal True --model_path path/to/model
# semantickitti
python test.py --dataset semantickitti --compress_normal True --model_path path/to/model

The decompressed patches and full point clouds will also be saved at ./output/experiment_id/pcd by default.

Acknowledgments

Our code is built upon the following repositories: DEPOCO, PAConv, Point Transformer and MCCNN, thanks for their great work.

Citation

If you find our project is useful, please consider citing:

@InProceedings{He_2022_CVPR,
    author    = {He, Yun and Ren, Xinlin and Tang, Danhang and Zhang, Yinda and Xue, Xiangyang and Fu, Yanwei},
    title     = {Density-preserving Deep Point Cloud Compression},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year      = {2022}
}