Dual Contouring of Signed Distance Data

2026-03-31

AI Summary

Reconstructing explicit meshes with sharp features from discretely sampled signed distance functions (SDFs) remains challenging, as existing approaches typically rely on gradient information or data-driven training. This work proposes an improved dual contouring method that formulates a quadratic optimization problem within each regular grid cell using only SDF sample values to determine optimal vertex positions, eliminating the need for gradients or large-scale training data. To the best of our knowledge, this is the first method to achieve high-quality reconstruction of sharp geometric features using solely discrete SDF samples. It significantly outperforms current techniques at medium to high resolutions, offering a more robust and efficient solution for surface reconstruction in 3D modeling and design.

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.

Problem

• Signed Distance Function
• Surface Reconstruction
• Sharp Features
• Polygonal Mesh
• Dual Contouring

Innovation

• Dual Contouring
• Signed Distance Function
• Sharp Feature Recovery
• Quadratic Optimization
• Surface Reconstruction