Google Research

Deep Implicit Volume Compression

CVPR (2020)


We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in voxel grids and their corresponding textures. To compress the TSDF our method relies on a block-based neural architecture trained end-to-end achieving state-of-the-art compression rates. To prevent topological errors we losslessly compress the signs of the TSDF which also as a side effect bounds the maximum reconstruction error by the voxel size. To compress the affiliated texture we designed a fast block-base charting and Morton packing technique generating a coherent image that can be efficiently compressed using existing image-based compression algorithms. We demonstrate the performance of our algorithms on a large set of 4D performance sequences captured using multi-camera RGBD setups.

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work