Article

Geometry images

Authors:

Steven J. Gortler,

Hugues HoppeAuthors Info & Claims

SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques

Pages 355 - 361

https://doi.org/10.1145/566570.566589

Published: 01 July 2002 Publication History

Abstract

Surface geometry is often modeled with irregular triangle meshes. The process of remeshing refers to approximating such geometry using a mesh with (semi)-regular connectivity, which has advantages for many graphics applications. However, current techniques for remeshing arbitrary surfaces create only semi-regular meshes. The original mesh is typically decomposed into a set of disk-like charts, onto which the geometry is parametrized and sampled. In this paper, we propose to remesh an arbitrary surface onto a completely regular structure we call a geometry image. It captures geometry as a simple 2D array of quantized points. Surface signals like normals and colors are stored in similar 2D arrays using the same implicit surface parametrization --- texture coordinates are absent. To create a geometry image, we cut an arbitrary mesh along a network of edge paths, and parametrize the resulting single chart onto a square. Geometry images can be encoded using traditional image compression algorithms, such as wavelet-based coders.

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Cited By

Lei NLi ZXu ZLi YGu X(2024)What's the Situation With Intelligent Mesh Generation: A Survey and PerspectivesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328178130:8(4997-5017)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3281781
Gallucci AZnamenskiy DLong YPezzotti NPetkovic M(2023)Generating High-Resolution 3D Faces and Bodies Using VQ-VAE-2 with PixelSNAIL Networks on 2D RepresentationsSensors10.3390/s2303116823:3(1168)Online publication date: 19-Jan-2023
https://doi.org/10.3390/s23031168
Otto CNaruniec JHelminger LEtterlin TMignone GChandran PZoss GSchroers CGross MGotardo PBradley DWeber R(2023)Learning Dynamic 3D Geometry and Texture for Video Face SwappingComputer Graphics Forum10.1111/cgf.1470541:7(611-622)Online publication date: 20-Mar-2023
https://doi.org/10.1111/cgf.14705
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Index Terms

Geometry images
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Parametric curve and surface models
      2. Volumetric models
2. Theory of computation
  1. Randomness, geometry and discrete structures

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cover image ACM Conferences

SIGGRAPH '02: Proceedings of the 29th annual conference on Computer graphics and interactive techniques

July 2002

574 pages

ISBN:1581135211

DOI:10.1145/566570

Conference Chair:
Tom Appolloni
Harris Corporation

Copyright © 2002 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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New York, NY, United States

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Published: 01 July 2002

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SIGGRAPH02

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SIGGRAPH

SIGGRAPH02: The 29th International Conference on Computer Graphics and Interactive Techniques

July 23 - 26, 2002

Texas, San Antonio

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SIGGRAPH '02 Paper Acceptance Rate 67 of 358 submissions, 19%;

Overall Acceptance Rate 1,822 of 8,601 submissions, 21%

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Citations

Cited By

Lei NLi ZXu ZLi YGu X(2024)What's the Situation With Intelligent Mesh Generation: A Survey and PerspectivesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328178130:8(4997-5017)Online publication date: Aug-2024
https://doi.org/10.1109/TVCG.2023.3281781
Gallucci AZnamenskiy DLong YPezzotti NPetkovic M(2023)Generating High-Resolution 3D Faces and Bodies Using VQ-VAE-2 with PixelSNAIL Networks on 2D RepresentationsSensors10.3390/s2303116823:3(1168)Online publication date: 19-Jan-2023
https://doi.org/10.3390/s23031168
Otto CNaruniec JHelminger LEtterlin TMignone GChandran PZoss GSchroers CGross MGotardo PBradley DWeber R(2023)Learning Dynamic 3D Geometry and Texture for Video Face SwappingComputer Graphics Forum10.1111/cgf.1470541:7(611-622)Online publication date: 20-Mar-2023
https://doi.org/10.1111/cgf.14705
Fan ZZhang ZYang SZhong CCao MXia S(2023)Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01904(20771-20781)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.01904
Zhang CHe BGuo RMa D(2023)When a tree model meets texture baking: an approach for quality-preserving lightweight visualization in virtual 3D scene constructionInternational Journal of Digital Earth10.1080/17538947.2023.217775816:1(645-670)Online publication date: Mar-2023
https://doi.org/10.1080/17538947.2023.2177758
Yang YLu KWu YWang YCao Y(2023)Correlation-aware probabilistic data summarization for large-scale multi-block scientific data visualizationComputational Visual Media10.1007/s41095-022-0304-69:3(513-529)Online publication date: 18-Mar-2023
https://doi.org/10.1007/s41095-022-0304-6
Asgharian LEbrahimnezhad H(2023)3D model representation using space curves: an efficient mesh simplification method by exchanging triangulated mesh to space curvesMultimedia Tools and Applications10.1007/s11042-023-14777-482:20(30965-31000)Online publication date: 22-Feb-2023
https://doi.org/10.1007/s11042-023-14777-4
Finley MSchwartz BNishimura JKubicek BBell T(2022)SCDeep: Single-Channel Depth Encoding for 3D-Range Geometry Compression Utilizing Deep-Learning TechniquesPhotonics10.3390/photonics90704499:7(449)Online publication date: 27-Jun-2022
https://doi.org/10.3390/photonics9070449
Schwartz BFinley MBell T(2022)Foveated 3D range geometry compression via loss-tolerant variable precision depth encodingApplied Optics10.1364/AO.47235661:33(9911)Online publication date: 15-Nov-2022
https://doi.org/10.1364/AO.472356
Jin YChen ZLu YYang JLiu YShi Z(2022)Research on Multi-Precision Fabric Modeling Method Based on Machine LearningScientific Programming10.1155/2022/43390952022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/4339095
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