Reach For the Spheres: Tangency-Aware Surface Reconstruction of SDFs

TL;DR

This paper introduces a tangency-aware surface reconstruction method for SDFs that overcomes oversmoothing issues of traditional techniques by leveraging the geometric property that SDF samples are tangent to the surface, resulting in more accurate reconstructions.

Contribution

The authors propose a novel energy formulation based on tangency constraints and a gradient flow optimization, improving surface reconstruction quality from sparse SDF samples.

Findings

Outperforms traditional isosurfacing methods like Marching Cubes.

Produces more accurate surface reconstructions with fewer samples.

Offers a deeper geometric understanding of SDFs for better reconstruction.

Abstract

Signed distance fields (SDFs) are a widely used implicit surface representation, with broad applications in computer graphics, computer vision, and applied mathematics. To reconstruct an explicit triangle mesh surface corresponding to an SDF, traditional isosurfacing methods, such as Marching Cubes and and its variants, are typically used. However, these methods overlook fundamental properties of SDFs, resulting in reconstructions that exhibit severe oversmoothing and feature loss. To address this shortcoming, we propose a novel method based on a key insight: each SDF sample corresponds to a spherical region that must lie fully inside or outside the surface, depending on its sign, and that must be tangent to the surface at some point. Leveraging this understanding, we formulate an energy that gauges the degree of violation of tangency constraints by a proposed surface. We then employ a gradient flow that minimizes our energy, starting from an initial triangle mesh that encapsulates the surface. This algorithm yields superior reconstructions to previous methods, even with sparsely sampled SDFs. Our approach provides a more nuanced understanding of SDFs and offers significant improvements in surface reconstruction.