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

  • Peilin Chen*
  • , Shiqi Wang
  • , Zhu Li
  • *此作品的通讯作者
  • City University of Hong Kong
  • University of Missouri-KC

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

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.

源语言英语
主期刊名Proceedings - DCC 2023
主期刊副标题2023 Data Compression Conference
编辑Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
出版商Institute of Electrical and Electronics Engineers Inc.
332
页数1
ISBN(电子版)9798350347951
DOI
出版状态已出版 - 2023
已对外发布
活动2023 Data Compression Conference, DCC 2023 - Snowbird, 美国
期限: 21 3月 202324 3月 2023

出版系列

姓名Data Compression Conference Proceedings
2023-March
ISSN(印刷版)1068-0314

会议

会议2023 Data Compression Conference, DCC 2023
国家/地区美国
Snowbird
时期21/03/2324/03/23

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