收藏切换
Research Progress and Review of Dynamic Point Cloud Compression Technology
收藏切换
Bailin Yang1), Ruiqi Ying1), Dandan Ding2), Fangzhe Nan3), Junjie Zhao4), Dong Zheng5)
Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12) : 2033 - 2051
Less
收藏切换
Journal of Computer-Aided Design & Computer Graphics | 2025, 37(12): 2033-2051
Research Progress and Review of Dynamic Point Cloud Compression Technology
Full
Bailin Yang1), Ruiqi Ying1), Dandan Ding2), Fangzhe Nan3), Junjie Zhao4), Dong Zheng5)
Affiliations
  • 1)School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018
  • 2)School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121
  • 3)School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018
  • 4)China Mobile (Hangzhou) Information Technology Company Limited, Hangzhou 310023
  • 5)Universal Ubiquitous Co., Ltd., Hangzhou 311121
Published: 2025-12-20 doi: 10.3724/SP.J.1089.2024-00699
Outline
收藏切换

Dynamic point cloud data, characterized by temporal continuity and high-dimensional features, can accurately capture dynamic changes of objects and are thus widely applied in fields such as autonomous driving, augmented reality, virtual reality, robotic navigation, and 3D video. Dynamic point cloud compression plays a crucial role in efficiently managing the storage, transmission, and perception of the ever-increasing volume of point cloud data. Despite this demand, there remains a clear lack of comprehensive surveys specifically focused on dynamic point cloud compression methods. To address this gap, this paper presents a systematic review of existing approaches, aiming to summarize recent advancements in the field and provide insights for future research. The paper first introduces the significance and theoretical foundations of dynamic point cloud compression. It then elaborates on the fundamental principles and advantages of mainstream models from three perspectives: geometry compression, attribute compression, and joint compression. Subsequently, commonly used datasets and evaluation metrics are reviewed, and the performance of various methods across different datasets is summarized. Finally, the paper analyzes the limitations of current methods in terms of algorithmic complexity and scene adaptability, and points out that lightweight network design, joint spatiotemporal modeling, and cross-modal information fusion are major directions for future research. This survey is expected to facilitate a deeper understanding of dynamic point cloud compression and promote further development of related systems.

dynamic point cloud compression  /  deep learning  /  geometric compression  /  attribute compression  /  joint compression
杨柏林, 应瑞琦, 丁丹丹, 南方哲, 赵俊杰, 郑东. 动态点云压缩技术的研究发展与综述. 计算机辅助设计与图形学学报, 2025 , 37 (12) : 2033 -2051 . DOI: 10.3724/SP.J.1089.2024-00699
Bailin Yang, Ruiqi Ying, Dandan Ding, Fangzhe Nan, Junjie Zhao, Dong Zheng. Research Progress and Review of Dynamic Point Cloud Compression Technology[J]. Journal of Computer-Aided Design & Computer Graphics, 2025 , 37 (12) : 2033 -2051 . DOI: 10.3724/SP.J.1089.2024-00699
Year 2025 volume 37 Issue 12
105
20
Cite this Article
BibTeX
Article Info
doi: 10.3724/SP.J.1089.2024-00699
  • Receive Date:2024-11-16
  • Online Date:2026-10-10
  • Published:2025-12-20
Article Data
Affiliations
History
  • Received:2024-11-16
  • Revised:2025-06-24
Funding
Affiliations
    1)School of Computer Science and Technology, Zhejiang Gongshang University, Hangzhou 310018
    2)School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121
    3)School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018
    4)China Mobile (Hangzhou) Information Technology Company Limited, Hangzhou 310023
    5)Universal Ubiquitous Co., Ltd., Hangzhou 311121
References
Share
https://castjournals.cast.org.cn/joweb/jsjfzsj/EN/10.3724/SP.J.1089.2024-00699
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT