SpUDD: Superpower Contouring of Unsigned Distance Data

TL;DR

This paper introduces SpUDD, a novel method for reconstructing surfaces from unsigned distance data using superpower contours, which guarantees convergence and outperforms existing approaches.

Contribution

The paper proposes the superpower contour concept and an algorithm that effectively reconstructs surfaces from discrete unsigned distance samples, a previously challenging problem.

Findings

Superpower contour converges to the true surface with increasing sampling density.

The proposed method outperforms existing strategies in unsigned distance reconstruction.

The approach sets a foundation for future research in this mathematically rich area.

Abstract

Unsigned distance functions offer a powerful and flexible implicit surface representation that, unlike their signed counterparts, allow for surfaces that are open, non-orientable, or non-manifold. We consider the problem of reconstructing arbitrary surfaces from a finite set of samples of unsigned distance data. Existing methods for mesh reconstruction from distance data rely on sign information, accurate gradients, a corresponding continuous distance function, or extensive data-dependent training. However, they fail when applied to input that is both discrete and unsigned. Inspired by this challenge, we study the power diagram generated by the distance samples and propose a novel theoretical concept, the superpower contour, which we prove converges to the true surface in the limit of sampling density. We use this superpower contour as an initial surface proxy and design an algorithm that leverages it to produce a polygonal mesh approximating the unknown true geometry. Our method vastly outperforms other conceivable strategies for the discrete unsigned distance reconstruction task, and sets the stage for future work on this mathematically rich problem.