Deep Implicit Volume Compression

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 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.

Danhang Tang and Saurabh Singh have equal contribution

References in corpus (5)

• Learned Point Cloud Geometry Compression
• Variable Rate Image Compression with Recurrent Neural Networks
• Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds
• A Volumetric Approach to Point Cloud Compression
• An Eigenshapes Approach to Compressed Signed Distance Fields and Their Utility in Robot Mapping

Cited by in corpus (4)

• Better Patch Stitching for Parametric Surface Reconstruction
• A deep perceptual metric for 3D point clouds
• 3D Shapes Local Geometry Codes Learning with SDF
• DeepCompress: Efficient Point Cloud Geometry Compression