Dual Contouring of Signed Distance Data

TL;DR

This paper introduces a novel algorithm for reconstructing explicit polygonal meshes from discretely sampled Signed Distance Function data, effectively capturing sharp features without needing gradient or large dataset access.

Contribution

It extends Dual Contouring by formulating and solving a quadratic optimization problem for vertex placement based solely on sampled SDF data.

Findings

Sets new state of the art in surface reconstruction quality.

Effective at recovering sharp features in 3D models.

Operates without requiring gradient information or large datasets.

Abstract

We propose an algorithm to reconstruct explicit polygonal meshes from discretely sampled Signed Distance Function (SDF) data, which is especially effective at recovering sharp features. Building on the traditional Dual Contouring of Hermite Data method, we design and solve a quadratic optimization problem to decide the optimal placement of the mesh's vertices within each cell of a regular grid. Critically, this optimization relies solely on discretely sampled SDF data, without requiring arbitrary access to the function, gradient information, or training on large-scale datasets. Our method sets a new state of the art in surface reconstruction from SDFs at medium and high resolutions, and opens the door for applications in 3D modeling and design.