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Occupancy Map Guided Attributes Deblocking for Video-based Point Cloud Compression

  • Peilin Chen*
  • , Shiqi Wang
  • , Zhu Li
  • *Corresponding author for this work
  • City University of Hong Kong
  • University of Missouri-KC

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Point clouds offer the realistic three-dimensional (3-D) representation of objects or scenes at the expense of high data volume. To compactly represent such data in real-world applications, Video-based Point Cloud Compression (V-PCC) converts them into two-dimensional (2-D) attribute maps before lossy compression. However, the coding artifacts introduced in the decoded attribute maps eventually bring texture degradation in the reconstructed point cloud. In this paper, we propose a deep-learning based attribute map enhancement method by fully leveraging the guidance of the occupancy map in local feature modification and non-local attention for capturing long-range spatial correlations.

Original languageEnglish
Title of host publicationProceedings - DCC 2023
Subtitle of host publication2023 Data Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages332
Number of pages1
ISBN (Electronic)9798350347951
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 Data Compression Conference, DCC 2023 - Snowbird, United States
Duration: 21 Mar 202324 Mar 2023

Publication series

NameData Compression Conference Proceedings
Volume2023-March
ISSN (Print)1068-0314

Conference

Conference2023 Data Compression Conference, DCC 2023
Country/TerritoryUnited States
CitySnowbird
Period21/03/2324/03/23

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