ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction

• URL: http://arxiv.org/abs/2609.06723v1
• Date: Sun, 06 Sep 2026 16:55:21 GMT
• Title: ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction
• Abstract summary: Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines.<n>Existing mesh-native optimization methods alleviate some of these issues but suffer from fixed-resolution discretizations and unstable optimization behavior.<n>We introduce an adaptive mesh-based optimization framework and a practical mesh rendering technique to address these challenges.
• Score: 10.718334749514694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines, yet existing reconstruction methods struggle to produce high-quality meshes. Most state-of-the-art approaches initially learn an intermediate representation (NeRF/3DGS) and treat mesh extraction as a post-processing step, which often leads to oversmoothed surfaces or poor quality meshes with excessive triangle counts.Existing mesh-native optimization methods alleviate some of these issues but suffer from fixed-resolution discretizations and unstable optimization behavior. In this paper, we introduce an adaptive mesh-based optimization framework and a practical mesh rendering technique to address these challenges. Our representation combines an optimizable Delaunay-triangulated tetrahedral grid with a multi-resolution hash grid. The former is refined through point pruning and insertion, while the latter provides latent features for SDF/appearance value predictions. We use volumetric rendering to bootstrap a coarse geometry while leveraging mesh-based rendering for recovering fine-grained details. Additionally, we propose a differentiable, rasterization-based depth-offset rendering formulation, reducing geometric artifacts and improving reconstruction quality. Our method significantly outperforms existing mesh optimization approaches across a variety of object-centric benchmarks while being competitive with state-of-the-art NeRF/3DGS methods.

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