3D Compression Using Neural Fields
Janis Postels Yannick Strümpler Klara Reichard Luc Van Gool Federico Tombari
Neural Fields (NFs) have gained momentum as a tool for compressing various
data modalities - e.g. images and videos. This work leverages previous advances
and proposes a novel NF-based compression algorithm for 3D data. We derive two
versions of our approach - one tailored to watertight shapes based on Signed
Distance Fields (SDFs) and, more generally, one for arbitrary non-watertight
shapes using Unsigned Distance Fields (UDFs). We demonstrate that our method
excels at geometry compression on 3D point clouds as well as meshes. Moreover,
we show that, due to the NF formulation, it is straightforward to extend our
compression algorithm to compress both geometry and attribute (e.g. color) of
3D data.