research-article

Micro-Mesh Construction

Authors:

Andrea Maggiordomo,

Marco TariniAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 42, Issue 4

Article No.: 121, Pages 1 - 18

https://doi.org/10.1145/3592440

Published: 26 July 2023 Publication History

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.

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Barczak JBenthin CMcAllister D(2024)DGF: A Dense, Hardware-Friendly Geometry Format for Lossily Compressing Meshlets with Arbitrary TopologiesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36753837:3(1-17)Online publication date: 9-Aug-2024
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Index Terms

Micro-Mesh Construction
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Mesh geometry models

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 4

August 2023

1912 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3609020

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Copyright © 2023 Owner/Author(s).

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Published: 26 July 2023

Published in TOG Volume 42, Issue 4

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Cited By

Waldemarson GDoggett M(2024)Succinct Opacity MicromapsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36753857:3(1-18)Online publication date: 9-Aug-2024
https://dl.acm.org/doi/10.1145/3675385
Barczak JBenthin CMcAllister D(2024)DGF: A Dense, Hardware-Friendly Geometry Format for Lossily Compressing Meshlets with Arbitrary TopologiesProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36753837:3(1-17)Online publication date: 9-Aug-2024
https://dl.acm.org/doi/10.1145/3675383
Edavamadathil Sivaram VLi TRamamoorthi R(2024)Neural Geometry Fields For MeshesACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657399(1-11)Online publication date: 13-Jul-2024
https://dl.acm.org/doi/10.1145/3641519.3657399
Gruen HBenthin CKensler ABarczak JMcAllister D(2024)Ray Tracing Animated Displaced Micro‐MeshesComputer Graphics Forum10.1111/cgf.1522543:7Online publication date: 30-Oct-2024
https://doi.org/10.1111/cgf.15225
Weinreich CDe Oliveira LHoudard ANader G(2024)Real‐Time Neural Materials using Block‐Compressed FeaturesComputer Graphics Forum10.1111/cgf.1501343:2Online publication date: 23-Apr-2024
https://doi.org/10.1111/cgf.15013
Dou YZheng ZJin QShi RLi YNi B(2024)Differentiable Micro-Mesh Construction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00411(4294-4303)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00411
Bozhilov IPetkova RTonchev KManolova A(2024)A Systematic Survey Into Compression Algorithms for Three-Dimensional ContentIEEE Access10.1109/ACCESS.2024.346954912(141604-141624)Online publication date: 2024
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Ogaki S(2023)Nonlinear Ray Tracing for Displacement and Shell MappingSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618199(1-10)Online publication date: 10-Dec-2023
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Thonat TGeorgiev IBeaune FBoubekeur T(2023)RMIP: Displacement ray tracing via inversion and oblong boundingSIGGRAPH Asia 2023 Conference Papers10.1145/3610548.3618182(1-11)Online publication date: 10-Dec-2023
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