We propose a practical deep generative approach for lossless point cloud
geometry compression, called MSVoxelDNN, and show that it significantly reduces
the rate compared to the MPEG G-PCC codec. Our previous work based on
autoregressive models (VoxelDNN) has a fast training phase, however, inference
is slow as the occupancy probabilities are predicted sequentially, voxel by
voxel. In this work, we employ a multiscale architecture which models voxel
occupancy in coarse-to-fine order. At each scale, MSVoxelDNN divides voxels
into eight conditionally independent groups, thus requiring a single network
evaluation per group instead of one per voxel. We evaluate the performance of
MSVoxelDNN on a set of point clouds from Microsoft Voxelized Upper Bodies
(MVUB) and MPEG, showing that the current method speeds up encoding/decoding
times significantly compared to the previous VoxelDNN, while having average
rate saving over G-PCC of 17.5%. The implementation is available at
this https URL

提出了一种名为 MSVoxelDNN 的实用深度生成方法，用于无损点云几何压缩，并表明它与 MPEG G-PCC 编解码器相比大大降低了比特率。