Augmented Normalizing Flow for Point Cloud Geometry Coding

Siao Yu Li, Ji Jin Chiu, Jui Chiu Chiang, Wen Hsiao Peng, Wen Nung Lie

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

With the increased popularity of immersive media, point clouds have become one of the popular data representations for presenting 3D scenes. The huge amount of point cloud data poses a great challenge on their storage and real-time transmission, which calls for efficient point cloud compression. This paper presents a novel point cloud geometry compression technique based on learning end-to-end an augmented normalizing flow (ANF) model to represent the occupancy status of voxelized data points. The higher expressive power of ANF than variational autoencoders (V AE) is leveraged for the first time to represent binary occupancy status. Compared to two coding standards developed by MPEG, namely G-PCC (geometry-based point cloud compression) and V-PCC (video-based point cloud compression), our method achieves more than 80% and 30% bitrate reduction, respectively. Compared to several learning-based methods, our method also yields better performance.

原文English
主出版物標題2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781665475921
DOIs
出版狀態Published - 2022
事件2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022 - Suzhou, China
持續時間: 13 12月 202216 12月 2022

出版系列

名字2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022

Conference

Conference2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
國家/地區China
城市Suzhou
期間13/12/2216/12/22

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