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.
@article{arxiv.2005.08877,
title = {Deep Implicit Volume Compression},
author = {Danhang Tang and Saurabh Singh and Philip A. Chou and Christian Haene and Mingsong Dou and Sean Fanello and Jonathan Taylor and Philip Davidson and Onur G. Guleryuz and Yinda Zhang and Shahram Izadi and Andrea Tagliasacchi and Sofien Bouaziz and Cem Keskin},
journal= {arXiv preprint arXiv:2005.08877},
year = {2020}
}
Comments
Danhang Tang and Saurabh Singh have equal contribution