ADS: Random Sampling of Occupancy Functions using Adaptive Delaunay Scaffolding

2026-05-04

AI Summary

Existing methods struggle to simultaneously achieve unbiased stochastic sampling on implicit occupancy function surfaces and high-quality mesh reconstruction. This work proposes Adaptive Delaunay Sampling (ADS), which, for the first time, employs adaptive Delaunay tetrahedralization as a unified framework to concurrently generate nearly unbiased surface samples and topologically consistent isosurface meshes by refining tetrahedral edges that cross the surface. By integrating Marching Tetrahedra for mesh extraction with curvature-aware resampling, ADS achieves a superior trade-off between sampling accuracy and computational efficiency, requiring only a small number of function evaluations. Extensive experiments on 150 test objects validate its effectiveness, and the method demonstrates successful application in downstream tasks.

Abstract

Dense random sampling and surfacing of shapes encoded via implicit occupancy functions (OFs) are critical elements of many applications. Existing methods largely provide either one or the other of random sampling or mesh surfaces: ray shooting approaches deliver random samples with no connectivity, and grid-based methods deliver mesh surfaces but their sampling is highly biased. We propose a new method which delivers both pseudo-random OF surface samples and an isosurface mesh connecting them. Our method achieves these goals while requiring an order of magnitude fewer function evaluations than prior approaches. Key to our Adaptive Delaunay Sampling (ADS) approach is a progressively computed Delaunay tetrahedralization of points in 3D space, which we use as a sampling and surfacing scaffold. Starting from an initial coarse Delaunay scaffold, we repeatedly refine crossing edges, ones whose end vertices lie on opposite sides of the surface, augmenting the scaffold with points closer and closer to the surface. Each refinement step uses the Delaunay criterion to incorporate the newly added vertices into the scaffold, introducing new crossing edges. We use the intersections of fine crossing edges with the OF surface as the output samples, and use the marching tetrahedra method to surface these samples. We subsequently use normal estimation to densify the sampling near fine features and in areas of high surface curvature. We validate ADS by sampling 150 inputs at different resolutions, and provide extensive comparisons to existing alternatives. Our experiments demonstrate significant improvement in accuracy/function evaluation count trade-off, and showcase downstream applications.

Problem

• occupancy functions
• random sampling
• surface reconstruction
• isosurface mesh
• Delaunay tetrahedralization

Innovation

• Adaptive Delaunay Sampling
• Occupancy Functions
• Isosurface Meshing
• Random Surface Sampling
• Function Evaluation Efficiency