Nguyen DT, Quach M, Valenzise G, Duhamel P (2021)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2021
Pages Range: 4220-4224
Conference Proceedings Title: ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
URI: https://ieeexplore.ieee.org/abstract/document/9414763
DOI: 10.1109/ICASSP39728.2021.9414763
This paper presents a learning-based, lossless compression method for static point cloud geometry, based on context-adaptive arithmetic coding. Unlike most existing methods working in the octree domain, our encoder operates in a hybrid mode, mixing octree and voxel-based coding. We adaptively partition the point cloud into multi-resolution voxel blocks according to the point cloud structure, and use octree to signal the partitioning. On the one hand, octree representation can eliminate the sparsity in the point cloud. On the other hand, in the voxel domain, convolutions can be naturally expressed, and geometric information (i.e., planes, surfaces, etc.) is explicitly processed by a neural network. Our context model benefits from these properties and learns a probability distribution of the voxels using a deep convolutional neural network with masked filters, called VoxelDNN. Experiments show that our method outperforms the state-of-the-art MPEG G-PCC standard with average rate savings of 28% on a diverse set of point clouds from the Microsoft Voxelized Upper Bodies (MVUB) and MPEG. The implementation is available at https://github.com/Weafre/VoxelDNN.
APA:
Nguyen, D.T., Quach, M., Valenzise, G., & Duhamel, P. (2021). Learning-based lossless compression of 3d point cloud geometry. In ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4220-4224).
MLA:
Nguyen, Dat Thanh, et al. "Learning-based lossless compression of 3d point cloud geometry." Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2021. 4220-4224.
BibTeX: Download