Journal of South China University of Technology(Natural Science) >

2024 , Vol. 52 >Issue 6: 148 - 156

DOI: https://doi.org/10.12141/j.issn.1000-565X.230188

Computer Science & Technology

Point Cloud Geometry Coding Framework Based on Sampling

  • LIU Hao ,
  • YUAN Hui ,
  • CHEN Chen ,
  • GAO Wei
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  • 1.School of Control Science and Engineering，Shandong University，Jinan 250061，Shandong，China
    2.School of Computer and Control Engineering，Yantai University，Yantai 264005，Shandong，China
    3.Peking University Shenzhen Graduate School，Shenzhen 518055，Guangdong，China

Received date: 2023-04-06

  Online published: 2023-12-22

Supported by

the National Natural Science Foundation of China(62172259);the Natural Science Foundation of Shandong Province(ZR2023QF111);Hong Kong RGC Grants(CityU11202320)

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Abstract

With the development of science and technology, the accuracy of 3D point cloud acquisition equipment has been continuously improved, and the acquisition of massive 3D point cloud data has become a reality. However, the irregular distribution and huge number of data points of 3D point cloud bring great challenges to data storage and transmission. Therefore, 3D point cloud coding is imperative. From the perspective of data sampling, this paper transforms the 3D point cloud coding problem into a 3D point cloud sampling-reconstruction problem, and proposes a sampling-based 3D point cloud geometry coding framework. In this framework, firstly, the down-sampling method is used to sample the original 3D point cloud to the sparse 3D point cloud with a specified number of points. Then, the sparse 3D point cloud is encoded using any existing coding methods (the number of encoding points is significantly reduced, which can effectively reduce the encoding rate). Finally, by using the proposed upsampling method, the decoded sparse 3D point cloud is interpolated as a high-quality dense 3D point cloud similar to the shape of the original input point cloud. Experimental results show that, as compared with the latest G-PCC provided by MPEG, the proposed 3D point cloud geometry coding framework improves the objective quality of the reconstructed 3D point cloud by 5.49 dB on average, and presents better subjective visual effect.

Key words： point cloud; geometry coding; deep learning; sampling

Cite this article

LIU Hao , YUAN Hui , CHEN Chen , GAO Wei . Point Cloud Geometry Coding Framework Based on Sampling[J]. Journal of South China University of Technology(Natural Science), 2024 , 52(6) : 148 -156 . DOI: 10.12141/j.issn.1000-565X.230188

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