Geometry reconstruction for spatial scalability in point cloud compression based on neighbour occupancies

Zhang Chen; Shuai Wan; Zhecheng Wang

doi:10.1117/12.2625729

30 April 2022 Geometry reconstruction for spatial scalability in point cloud compression based on neighbour occupancies

Zhang Chen, Shuai Wan, Zhecheng Wang

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121772D (2022) https://doi.org/10.1117/12.2625729
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Spatial scalability is an important functionality for point cloud compression. The current design of geometry reconstruction for spatial scalability applies the points at the center of nodes, ignoring correlations among neighbour nodes. In this work, a geometry reconstruction method based on neighbour occupancies is proposed, where the distribution of real points in the current node is predicted using the information of neighbour occupancies. In comparison to the state-of-the-art geometry-based point cloud compression, i.e., G-PCC, performance improvement of 1.15dB in D1- PSNR and 3.80dB in D2-PSNR in average, can be observed using proposed method.

Citation Download Citation

Zhang Chen, Shuai Wan, and Zhecheng Wang "Geometry reconstruction for spatial scalability in point cloud compression based on neighbour occupancies", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121772D (30 April 2022); https://doi.org/10.1117/12.2625729

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available