# Overview and Comparison of AVS Point Cloud Compression Standard

arXiv: https://arxiv.org/abs/2602.08613 · HTML: https://mlsanity.com/paper/2602.08613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 112 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compression](https://mlsanity.com/paper/2202.06028/) (2022)
- [Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model](https://mlsanity.com/paper/2303.06519/) (2023)
- [Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey](https://mlsanity.com/paper/2308.12113/) (2023)
- [Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlook](https://mlsanity.com/paper/2311.02608/) (2023)
- [A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation](https://mlsanity.com/paper/2405.11903/) (2024)
- [The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machine](https://mlsanity.com/paper/2409.08130/) (2024)
- [Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgery](https://mlsanity.com/paper/2405.03314/) (2024)
- [Deep Geometry Post-Processing for Decompressed Point Clouds](https://mlsanity.com/paper/2204.13952/) (2022)
- [ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Vision](https://mlsanity.com/paper/2504.14240/) (2025)
- [Screen-based 3D Subjective Experiment Software](https://mlsanity.com/paper/2308.03698/) (2023)
- [A general framework for rotation invariant point cloud analysis](https://mlsanity.com/paper/2402.01331/) (2024)
- [LLM-PCGC: Large Language Model-based Point Cloud Geometry Compression](https://mlsanity.com/paper/2408.08682/) (2024)
- [EPContrast: Effective Point-level Contrastive Learning for Large-scale Point Cloud Understanding](https://mlsanity.com/paper/2410.17207/) (2024)
- [Randomized Ternary Search Tries](https://mlsanity.com/paper/1606.04042/) (2016)
- [A Rate Control Algorithm for Video-based Point Cloud Compression](https://mlsanity.com/paper/2205.11825/) (2022)
- [Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approach](https://mlsanity.com/paper/2403.05950/) (2024)

## Built on by (most cited first, 1 of 1)

- [LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting](https://mlsanity.com/paper/2603.28431/) (2026)

## Abstract (arXiv)

Point cloud is a prevalent 3D data representation format with significant application values in immersive media, autonomous driving, digital heritage protection, etc. However, the large data size of point clouds poses challenges to transmission and storage, which influences the wide deployments. Therefore, point cloud compression plays a crucial role in practical applications for both human and machine perception optimization. To this end, the Moving Picture Experts Group (MPEG) has established two standards for point cloud compression, including Geometry-based Point Cloud Compression (G-PCC) and Video-based Point Cloud Compression (V-PCC). In the meantime, the Audio Video coding Standard (AVS) Workgroup of China also have launched and completed the development for its first generation point cloud compression standard, namely AVS PCC. This new standardization effort has adopted many new coding tools and techniques, which are different from the other counterpart standards. This paper reviews the AVS PCC standard from two perspectives, i.e., the related technologies and performance comparisons.