Micro-Mesh Construction
Andrea Maggiordomo, Henry Moreton, Marco Tarini
Abstract
Micro-meshes (μ-meshes) are a new structured graphics primitive supporting a large increase in geometric fidelity, without commensurate memory and run-time processing costs, consisting of a base mesh enriched by a displacement map. A new generation of GPUs supports this structure with native hardware μ-mesh ray-tracing, that leverages a self-bounding, compressed displacement mapping scheme to achieve these efficiencies. In this paper, we present anautomatic method to convert an existing multi-million triangle mesh into this compact format, unlocking the advantages of the data representation for a large number of scenarios. We identify the requirements for high-quality μ-meshes, and show how existing re-meshing and displacement-map baking tools are ill-suited for their generation. Our method is based on a simplification scheme tailored to the generation of high-quality base meshes , optimized for tessellation and displacement sampling, in conjunction with algorithms for determining displacement vectors to control the direction and range of displacements. We also explore the optimization of μ-meshes for texture maps and the representation of boundaries. We demonstrate our method with extensive batch processing, converting an existing collection of high-resolution scanned models to the micro-mesh representation, providing an open-source reference implementation, and, as additional material, the data and an inspection tool.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Cited by top-tier papers3
- DJM: Compact Base Meshes for Displacement Mapping using Triangle JacobiansCongyi Zhang, Nicholas Vining, Yanhong Lin, Alireza Khatami et al.SIGGRAPH 2026
- Deep Inverse Shading: Consistent Albedo and Surface Detail Recovery via Generative RefinementJiacheng Wu, Ruiqi Zhang, Jie ChenAAAI 2026
- Differentiable Micro-Mesh ConstructionYishun Dou, Zhong Zheng, Qiaoqiao Jin, Rui Shi et al.CVPR 2024
Related papers
- Ray Tracing Structured AMR Data Using ExaBricksIngo Wald, Stefan Zellmann, Will Usher, Nate Morrical et al.IEEE VIS 2020 · 18 citations
- LTM: Lightweight Textured Mesh Extraction and Refinement of Large Unbounded Scenes for Efficient Storage and Real-Time RenderingJaehoon Choi, Rajvi Shah, Qinbo Li, Yipeng Wang et al.CVPR 2024
- NeuMIP: multi-resolution neural materialsAlexandr Kuznetsov, Krishna Mullia, Zexiang Xu, Milos Hasan et al.SIGGRAPH 2021 · 68 citations
- A Memory Efficient Encoding for Ray Tracing Large Unstructured DataIngo Wald, Nate Morrical, Stefan ZellmannIEEE VIS 2021 · 13 citations
- GenUDC: High Quality 3D Mesh Generation With Unsigned Dual Contouring RepresentationRuowei Wang, Jiaqi Li, Dan Zeng, Xueqi Ma et al.ACM MM 2024 · 2 citations
