@inproceedings{c7b597ab9f7044aeb99ac8f69d7ff48f,
title = "Weighted attribute prediction based on morton code for point cloud compression",
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.",
keywords = "Adjacent points, KNN, Morton code, Nearest neighbors, PCC, Point cloud",
author = "Lei Wei and Shuai Wan and Zexing Sun and Xiaobin Ding and Wei Zhang",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020 ; Conference date: 06-07-2020 Through 10-07-2020",
year = "2020",
month = jul,
doi = "10.1109/ICMEW46912.2020.9105953",
language = "英语",
series = "2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2020",
}