Gradient Distance Function

Hieu Le, Federico Stella, Benoît Guillard, Pascal Fua

Keywords: Unsigned Distance Field, 3D shape Generation, 3D representation

TL;DR: We show that directly learning gradient distance functions yields a general-purpose, surface-differentiable representation for non-watertight shapes

Abstract: Unsigned Distance Functions (UDFs) can be used to represent non-watertight surfaces in a deep learning framework. However, UDFs tend to be brittle and difficult to learn, in part because the surface is located exactly where the UDF is non-differentiable. In this work, we show that Gradient Distance Functions (GDFs) can remedy this by being differentiable at the surface while still being able to represent open surfaces. This is done by associating to each 3D point a 3D vector whose norm is taken to be the unsigned distance to the surface and whose orientation is taken to be the direction towards the closest surface point. We demonstrate the effectiveness of GDFs on ShapeNet Car, Multi-Garment, and 3D-Scene datasets with both single-shape reconstruction networks or categorical auto-decoders.

Submission Number: 8