UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach

Kangli Wang¹, Wei Gao^1,2*
(* Corresponding author)

¹SECE, Peking University
²Peng Cheng Laboratory, Shenzhen, China

📣 News

• [24-12-09] Our paper has been accepted to AAAI 2025.
• [25-03-08] We release lossless compression code.

Todo

• Release training code
• Release inference code
• Release the Paper
• Release checkpoint
• Simplify the code

📌 Introduction

We propose an efficient unified point cloud geometry compression framework UniPCGC. It is a lightweight framework that supports lossy compression, lossless compression, variable rate and variable complexity. First, we introduce the Uneven 8-Stage Lossless Coder (UELC) in the lossless mode, which allocates more computational complexity to groups with higher coding difficulty, and merges groups with lower coding difficulty. Second, Variable Rate and Complexity Module (VRCM) is achieved in the lossy mode through joint adoption of a rate modulation module and dynamic sparse convolution. Finally, through the dynamic combination of UELC and VRCM, we achieve lossy compression, lossless compression, variable rate and complexity within a unified framework. Compared to the previous state-of-the-art method, our method achieves a compression ratio (CR) gain of 8.1% on lossless compression, and a Bjontegaard Delta Rate (BD-Rate) gain of 14.02% on lossy compression, while also supporting variable rate and variable complexity.

Ilustration of the proposed UniPCGC framework.

🔑 Setup

Type the command for general installation

conda env create -f environment.yml

For the installation of MinkowskiEngine, see the official repository.

🧩 Dataset Preparation

Please refer to the following links to obtain the data. We thank these great works.

Datasets	Download Link
ShapeNet	Link
8iVFB	Link
Testdata	Baidu Netdisk (kkll)

🚀 Running

For lossless compression, run the following code to train

python train_lossless.py --dataset "your dataset dir" --lr 8e-4

run the following code to compress and decompress

python unicoder_lossless.py --filedir "your dataset dir" --ckptdir "your ckpt dir"

🔎 Contact

If your have any comments or questions, feel free to contact kangliwang@stu.pku.edu.cn.

👍 Acknowledgement

Thanks for their awesome works (PCGCv2 and MinkowskiEngine).

📘 Citation

Please consider citing our work as follows if it is helpful.

@article{Wang_Gao_2025,
title={UniPCGC: Towards Practical Point Cloud Geometry Compression via an Efficient Unified Approach},
volume={39},
url={https://ojs.aaai.org/index.php/AAAI/article/view/33387},
DOI={10.1609/aaai.v39i12.33387},
number={12},
journal={Proceedings of the AAAI Conference on Artificial Intelligence},
author={Wang, Kangli and Gao, Wei},
year={2025},
month={Apr.},
pages={12721-12729} }