Dual Contouring of Signed Distance Data
This addresses a domain-specific problem in 3D modeling and design by improving surface reconstruction from SDFs.
The paper tackles the problem of reconstructing polygonal meshes from discretely sampled Signed Distance Function data, achieving state-of-the-art results in recovering sharp features at medium and high resolutions.
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.