Point Cloud Compression via Constrained Optimal Transport
(Official Pytorch Implemention, the code is modified from D-PCC)
Point cloud compression (PCC) algorithms are typically designed to achieve the lowest possible distortion at a given low bit rate. However, the perceptual quality is often neglected. To tackle this, we innovatively regard PCC as a constrained optimal transport (COT) problem and proposea novel data-driven method to take the balance of distortion, perception, and the bit rate. Specifically, our method adopts a discriminator to measure the perceptual loss, and a generator to measure the optimal mapping from the original point cloud distribution to the reconstructed distribution.
- Quantitative results
- Qualitative results
- Install the following packages
python==3.7
torch==1.7.1
torchvision==0.8.2
CUDA==11.0
numpy==1.20.3
open3d==0.9.0.0
einops==0.3.2
scikit-learn==1.0.1
compressai
ninja
pickle
argparse
tensorboard
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
# shapenet
python train.py --dataset shapenet
# semantickitti
python train.py --dataset semantickitti
# shapenet
python test.py --dataset shapenet --model_path path/to/model
# semantickitti
python test.py --dataset semantickitti --model_path path/to/model
The decompressed patches and full point clouds will also be saved at ./output/experiment_id/pcd by default.
Our code is built upon the following repositories: D-PCC, DEPOCO, PAConv, Point Transformer and MCCNN, thanks for their great work.
If you find our project is useful, please consider citing: