Multiscale deep context modeling for lossless point cloud geometry compression

• URL: http://arxiv.org/abs/2104.09859v1
• Date: Tue, 20 Apr 2021 09:48:12 GMT
• Title: Multiscale deep context modeling for lossless point cloud geometry compression
• Abstract summary: MSVoxelDNN is a deep generative approach for point cloud geometry compression. It significantly reduces the rate compared to the MPEG G-PCC.
• Score: 11.69103847045569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 https://github.com/Weafre/MSVoxelDNN.

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