Overview and Comparison of AVS Point Cloud Compression Standard
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 16 of its 112 references that are in the map. First posted on arXiv 9 Feb 2026.
Builds on
16 of 112 references.
References · 16
- 2022OctAttention: Octree-Based Large-Scale Contexts Model for Point Cloud Compressioncited by 57
- 2023Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Modelcited by 25
- 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
- 2023Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlookcited by 24
- 2024A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentationcited by 14
- 2024The JPEG Pleno Learning-based Point Cloud Coding Standard: Serving Man and Machinecited by 9
- 2024Deep Learning-based Point Cloud Registration for Augmented Reality-guided Surgerycited by 6
- 2022Deep Geometry Post-Processing for Decompressed Point Cloudscited by 5
- 2025ROI-Guided Point Cloud Geometry Compression Towards Human and Machine Visioncited by 4
- 2023Screen-based 3D Subjective Experiment Softwarecited by 3
- 2024A general framework for rotation invariant point cloud analysiscited by 2
- 2024LLM-PCGC: Large Language Model-based Point Cloud Geometry Compressioncited by 2
4 more
- 2024EPContrast: Effective Point-level Contrastive Learning for Large-scale Point Cloud Understandingcited by 2
- 2016Randomized Ternary Search Triescited by 1
- 2022A Rate Control Algorithm for Video-based Point Cloud Compressioncited by 1
- 2024Classifying Objects in 3D Point Clouds Using Recurrent Neural Network: A GRU LSTM Hybrid Approachcited by 1
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Citing papers per year: 2026: 1.
1 paper.