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

2026-09-06

AI Summary

本文提出了一种自适应网格优化框架和实用的网格渲染技术，通过结合可优化的Delaunay三角化四面体网格与多分辨率哈希网格，解决了高质量网格生成的问题。

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.

Problem

• mesh reconstruction
• geometry reasoning
• high-quality meshes
• intermediate representation
• optimization instability

Innovation

• Adaptive Mesh Optimization
• Delaunay Triangulation
• Multi-Resolution Hash Grid
• Differentiable Depth-Offset Rendering