TY - GEN
T1 - Augmented Normalizing Flow for Point Cloud Geometry Coding
AU - Li, Siao Yu
AU - Chiu, Ji Jin
AU - Chiang, Jui Chiu
AU - Peng, Wen Hsiao
AU - Lie, Wen Nung
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Point cloud compression
KW - geometry
KW - deep learning
KW - flow-based compression
KW - augmented normalizing flows
UR - http://www.scopus.com/inward/record.url?scp=85147252200&partnerID=8YFLogxK
U2 - 10.1109/VCIP56404.2022.10008821
DO - 10.1109/VCIP56404.2022.10008821
M3 - Conference contribution
AN - SCOPUS:85147252200
T3 - 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
BT - 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Visual Communications and Image Processing, VCIP 2022
Y2 - 13 December 2022 through 16 December 2022
ER -