@inproceedings{5a66c81a4e2f4977b2bf83da3e31feb0,
title = "Geometry Reconstruction for Spatial Scalability in Point Cloud Compression Based on Neighbour Occupancies",
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.",
keywords = "geometry reconstruction, octree neighbour nodes, spatial correlation, spatial scalability",
author = "Zhang Chen and Shuai Wan and Zhecheng Wang",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2022 International Workshop on Advanced Imaging Technology, IWAIT 2022 ; Conference date: 04-01-2022 Through 06-01-2022",
year = "2022",
doi = "10.1117/12.2625729",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Masayuki Nakajima and Shogo Muramatsu and Jae-Gon Kim and Jing-Ming Guo and Qian Kemao",
booktitle = "International Workshop on Advanced Imaging Technology, IWAIT 2022",
}