Deep Implicit Volume Compression
Danhang Tang, Saurabh Singh, Philip A. Chou, Christian Häne, Mingsong Dou, Sean Fanello, Jonathan M. Taylor, Philip Davidson, Onur G. Guleryuz, Yinda Zhang, Shahram Izadi, Andrea Tagliasacchi, Sofien Bouaziz, Cem Keskin
Abstract
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures. To compress the TSDF, our method relies on a block-based neural network architecture trained end-to-end, achieving state-of-the-art rate-distortion trade-off. To prevent topological errors, we losslessly com- press the signs of the TSDF, which also upper bounds the reconstruction error by the voxel size. To compress the corresponding texture, we designed a fast block-based UV parameterization, generating coherent texture maps that can be effectively compressed using existing video compression algorithms. We demonstrate the performance of our algo- rithms on two 4D performance capture datasets, reducing bitrate by 66% for the same distortion, or alternatively re- ducing the distortion by 50% for the same bitrate, compared to the state-of-the-art.