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A multiresolution representation for massive meshes.

Eric Shaffer1, Michael Garland

  • 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA. shaffer1@cs.uiuc.edu

IEEE Transactions on Visualization and Computer Graphics
|March 8, 2005
PubMed
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We developed a novel external memory multiresolution surface representation for large polygonal meshes. This method efficiently handles massive datasets, enabling faster rendering and adaptive simplification for complex geometric data.

Area of Science:

  • Computer Graphics
  • Geometric Modeling
  • Data Structures

Background:

  • Previous methods for massive mesh representation are memory-intensive or rely on resampling.
  • Existing techniques struggle with scalability for large polygonal datasets.

Purpose of the Study:

  • To introduce a new external memory multiresolution surface representation for massive polygonal meshes.
  • To overcome limitations of existing scalable mesh processing techniques.

Main Methods:

  • Developed a novel surface representation combining sampled and original surface data.
  • Designed a memory-insensitive construction algorithm for large-scale meshes.
  • Integrated multiresolution capabilities for efficient geometric operations.

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Main Results:

  • The representation processes meshes with hundreds of millions of polygons efficiently.
  • Enabled development of algorithms for view-dependent rendering, approximate collision detection, and adaptive simplification.
  • Demonstrated the data structure's power and flexibility for massive geometric data.

Conclusions:

  • The proposed external memory multiresolution surface representation is a scalable and efficient solution for massive meshes.
  • This approach facilitates advanced geometric processing tasks on large datasets.
  • The data structure offers a powerful and flexible tool for computer graphics and geometric modeling applications.