  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 compress 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
algorithms on two 4D performance capture datasets, reducing bitrate by 66% for
the same distortion, or alternatively reducing the distortion by 50% for the
same bitrate, compared to the state-of-the-art.
