Weighted attribute prediction based on morton code for point cloud compression

Lei Wei, Shuai Wan, Zexing Sun, Xiaobin Ding, Wei Zhang

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

11 Scopus citations

Abstract

The huge amount of data contained in the point cloud restrains its applications in practice. To compress the point cloud, the spatial correlation in the point cloud is explored, where adjacent points are searched and used for prediction. In this paper, an adjacent points searching method based on the Mor-ton code to find proper adjacent points is proposed, based on which a weighted prediction is performed. The proposed method locates the adjacent points quickly and accurately using the Morton code of the point cloud coordinate. Experimental results show that the proposed method significantly reduces the computational complexity while maintaining similar performance as the state-of-the-art.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728114859
DOIs
StatePublished - Jul 2020
Event2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020 - London, United Kingdom
Duration: 6 Jul 202010 Jul 2020

Publication series

Name2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020

Conference

Conference2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020
Country/TerritoryUnited Kingdom
CityLondon
Period6/07/2010/07/20

Keywords

  • Adjacent points
  • KNN
  • Morton code
  • Nearest neighbors
  • PCC
  • Point cloud

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