Introduction to Point Cloud Compression

doi:10.19729/j.cnki.1673-5188.2018.03.002

ZTE Communications ›› 2018, Vol. 16 ›› Issue (3): 3-8.DOI: 10.19729/j.cnki.1673-5188.2018.03.002

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Introduction to Point Cloud Compression

XU Yiling, ZHANG Ke, HE Lanyi, JIANG Zhiqian, ZHU Wenjie

Cooperative MediaNet Innovation Center, Shanghai Jiao Tong University, Shanghai 200240, China

Online:2018-08-25 Published:2020-03-18
About author:XU Yiling received her B.S., M.S., and Ph.D. from the University of Electronic Sci-ence and Technology of China in 1999, 2001, and 2004, respectively. She is a full researcher at Shanghai Jiaotong University, China. From 2004 to 2013, she was with the Multimedia Communication Research Institute of Samsung Electronics, Korea.Her research interests mainly include architecture design for next generation multi-media systems, cross-layer design, and dynamic adaptation for heterogeneous net-works.|ZHANG Ke (zhang_ke@sjtu.edu.cn) received the B.S. degree from Nanjing University of Science and Technology, China in 2016. He is pursuing the master degree in the Cooperative Medianet Innovation Center of Shanghai Jiao Tong University, China. His research interest is about point cloud compression and video coding.|HE Lanyi (hly92711@sjtu.edu.cn) received the B.S. degree in electronics and information engineering from Xidian University, China in 2015. He is pursuing the master degree in the Cooperative Medianet Innovation Center of Shanghai Jiao Tong University, China. His research interest is about point cloud coding.|JIANG Zhiqian (zhiqjiang@sjtu.edu.cn) received the B.S. degree in electronics and information engineering from Xidian University, China in 2015. He is pursuing the Ph.D degree in the Cooperative Medianet Innovation Center of Shanghai Jiao Tong University, China. His research interests include reliable transmission and immersive media transmission.|ZHU Wenjie (rebecca28@sjtu.edu.cn) received the B.S. degree in electronics and information engineering from Harbin Institute of Technology, China in 2014. She is pursuing the Ph.D degree in the Cooperative Medianet Innovation Center of Shanghai Jiao Tong University, China. Currently, she is studying in University of Missouri, Kansas City as an exchange scholar under the support of China Scholarship Council. Her research interests including point cloud compression and immersive media transportation. She has published around 6 conference papers and has 4 patents in processing with 3 MPEG proposals been accepted in international standard.

Abstract

Abstract:

Characterized by geometry and photometry attributes, point cloud has been widely applied in the immersive services of various 3D objects and scenes. The development of even more precise capture devices and the increasing requirements for vivid rendering inevitably induce huge point capacity, thus making the point cloud compression a demanding issue. In this paper, we introduce several well-known compression algorithms in the research area as well as the boosting industry standardization works. Specifically, based on various applications of this 3D data, we summarize the static and dynamic point cloud compression, both including irregular geometry and photometry information that represent the spatial structure information and corresponding attributes, respectively. In the end, we conclude the point cloud compression as a promising topic and discuss trends for future works.

Key words: immersive services, point cloud compression, geometry and photometry

XU Yiling, ZHANG Ke, HE Lanyi, JIANG Zhiqian, ZHU Wenjie. Introduction to Point Cloud Compression[J]. ZTE Communications, 2018, 16(3): 3-8.

Figures/Tables 9

Figure 1. Data acquisition devices.

Figure 2. Examples of 3D point cloud.

Figure 3. Octree decomposition.

Figure 4. Binary tree based partition and representation.

Figure 5. Adapted PAQ8 compression procedure.

Figure 6. Overview of the clustering based point cloud compression scheme.

Figure 7. Motion-compensated compression encoder.

Figure 8. Overview of the text model category 2 (TMC2v0) compression process.

Figure 9. Overview of the text model category 2 (TMC2v0) decompression process.

References 21

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Introduction to Point Cloud Compression

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