Learning-based lossless compression of 3d point cloud geometry

Dat Thanh Nguyen (1) , Maurice Quach (1) , Giuseppe Valenzise (1) , Pierre Duhamel (1)

1 L2S - Laboratoire des signaux et systèmes

This paper presents a learning-based, lossless compression method for static point cloud geometry, based on contextadaptive 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.

Mots clés: Point Cloud Compression; Deep Learning; G-PCC; context model

Domaines: Traitement du signal et de l'image [eess.SP]

Dat Thanh Nguyen, Maurice Quach, Giuseppe Valenzise, Pierre Duhamel. Learning-based lossless compression of 3d point cloud geometry. IEEE International Conference on Acoustics, Speech, and Signal Processing, Jun 2021, Toronto, Canada. ⟨10.1109/icassp39728.2021.9414763⟩. ⟨hal-03141577⟩