research-article

Simplification and Repair of Polygonal Models Using Volumetric Techniques

Authors:

Fakir S. Nooruddin,

Greg TurkAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 9, Issue 2

Pages 191 - 205

https://doi.org/10.1109/TVCG.2003.1196006

Published: 01 April 2003 Publication History

Abstract

Two important tools for manipulating polygonal models are simplification and repair and we present voxel-based methods for performing both of these tasks. We describe a method for converting polygonal models to a volumetric representation in a way that handles models with holes, double walls, and intersecting parts. This allows us to perform polygon model repair simply by converting a model to and from the volumetric domain. We also describe a new topology-altering simplification method that is based on 3D morphological operators. Visually unimportant features such as tubes and holes may be eliminated from a model by the open and close morphological operators. Our simplification approach accepts polygonal models as input, scan converts these to create a volumetric description, performs topology modification, and then converts the results back to polygons. We then apply a topology-preserving polygon simplification technique to produce a final model. Our simplification method produces results that are everywhere manifold.

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Index Terms

Simplification and Repair of Polygonal Models Using Volumetric Techniques
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
      2. Computer vision representations
        Shape representations

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cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 9, Issue 2

April 2003

159 pages

ISSN:1077-2626

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Copyright © 2003.

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IEEE Educational Activities Department

United States

Publication History

Published: 01 April 2003

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Cited By

Gaikwad BPatra ACrawford CMiller E(2025)Self-supervised anomaly detection and localization for X-ray cargo imagesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109675140:COnline publication date: 15-Jan-2025
https://dl.acm.org/doi/10.1016/j.engappai.2024.109675
Wang LWang XWang PChen SXin SGuo JWang WTu C(2024)PCO: Precision-Controllable Offset Surfaces with Sharp FeaturesACM Transactions on Graphics10.1145/368792043:6(1-16)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687920
Spainhour JGunderman DWeiss K(2024)Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding NumbersACM Transactions on Graphics10.1145/365822843:4(1-14)Online publication date: 19-Jul-2024
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Zheng ZGao XPan ZLi WWang PWang GWu K(2024)Visual-Preserving Mesh RepairIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334882930:9(6586-6597)Online publication date: 1-Jan-2024
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Zhao CMelkote S(2024)Learning the manufacturing capabilities of machining and finishing processes using a deep neural network modelJournal of Intelligent Manufacturing10.1007/s10845-023-02134-z35:4(1845-1865)Online publication date: 1-Apr-2024
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Böhm SNeumayer MZagar BRiß FKortüm CKnoll A(2024)Advancing 3D Mesh Analysis: A Graph Learning Approach for Intersecting 3D Geometry ClassificationPattern Recognition10.1007/978-3-031-78166-7_10(143-159)Online publication date: 1-Dec-2024
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Lamas-Fernandez CBennell JMartinez-Sykora A(2023)Voxel-Based Solution Approaches to the Three-Dimensional Irregular Packing ProblemOperations Research10.1287/opre.2022.226071:4(1298-1317)Online publication date: 1-Jul-2023
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