4.5 Article

A Comprehensive Study and Comparison of Core Technologies for MPEG 3-D Point Cloud Compression

Journal

IEEE TRANSACTIONS ON BROADCASTING
Volume 66, Issue 3, Pages 701-717

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBC.2019.2957652

Keywords

Three-dimensional displays; Geometry; Image color analysis; Octrees; Transform coding; Image coding; Solid modeling; 3D point clouds; MPEG compression standard; compression performance; virtual; augmented reality

Funding

  1. National Key Research and Development Program of China [2018YFC0831003]
  2. National Natural Science Foundation of China [61571274, 61871342]
  3. Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University [VRLAB2019B03]
  4. Shandong Natural Science Funds for Distinguished Young Scholar [JQ201614]
  5. Shandong Provincial Key Research and Development Plan [2017CXGC1504]
  6. Young Scholars Program of Shandong University [2015WLJH39]

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Point cloud based 3D visual representation is becoming popular due to its ability to exhibit the real world in a more comprehensive and immersive way. However, under a limited network bandwidth, it is very challenging to communicate this kind of media due to its huge data volume. Therefore, the MPEG have launched the standardization for point cloud compression (PCC), and proposed three model categories, i.e., TMC1, TMC2, and TMC3. Because the 3D geometry compression methods of TMC1 and TMC3 are similar, TMC1 and TMC3 are further merged into a new platform namely TMC13. In this paper, we first introduce some basic technologies that are usually used in 3D point cloud compression, then review the encoder architectures of these test models in detail, and finally analyze their rate distortion performance as well as complexity quantitatively for different cases (i.e., lossless geometry and lossless color, lossless geometry and lossy color, lossy geometry and lossy color) by using 16 benchmark 3D point clouds that are recommended by MPEG. Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity.

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