Perceptually guided polygon reduction

Lijun Qu; Gary W. Meyer

doi:10.1109/TVCG.2008.51

Perceptually guided polygon reduction

Lijun Qu, Gary W. Meyer

Computer Science and Engineering

Research output: Contribution to journal › Article › peer-review

44 Scopus citations

Abstract

The properties of the human visual system are taken into account, along with the geometric aspects of an object, in a new surface remeshing algorithm and a new mesh simplification algorithm. Both algorithms have a preprocessing step and are followed by the remeshing or mesh simplification steps. The preprocessing step computes an importance map that indicates the visual masking potential of the visual patterns on the surface. The importance map is then used to guide the remeshing or mesh simplification algorithms. Two different methods are proposed for computing an importance map that indicates the masking potential of the visual patterns on the surface. The first one is based on the Sarnoff visual discrimination metric, and the second one is inspired by the visual masking tool available in the current JPEG2000 standard. Given an importance map, the surface remeshing algorithm automatically distributes few samples to surface regions with strong visual masking properties due to surface texturing, lighting variations, bump mapping, surface reflectance, and interreflections. Similarly, the mesh simplification algorithm simplifies more aggressively where the light field of an object can hide more geometric artifacts.

Original language	English (US)
Article number	4479454
Pages (from-to)	1015-1029
Number of pages	15
Journal	IEEE Transactions on Visualization and Computer Graphics
Volume	14
Issue number	5
DOIs	https://doi.org/10.1109/TVCG.2008.51
State	Published - Sep 2008

Bibliographical note

Funding Information:
The authors would like to thank all anonymous reviewers for their helpful comments and suggestions, which have significantly improved this paper. They also want to thank Michael Garland for making his QSlim software available, and Cyberware and Stanford Graphics Lab for making the Igea and Bunny models available. The assistance of Jon Konieczny in running timing tests is also gratefully acknowledged. Funding was provided by the National Science Foundation Grant CCR-0242757. This research was performed at the University of Minnesota Digital Technology Center.

Keywords

Level of detail
Perceptually guided rendering
Simplification
Surface remeshing
Visual masking
Visual perception

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10.1109/TVCG.2008.51

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abstract = "The properties of the human visual system are taken into account, along with the geometric aspects of an object, in a new surface remeshing algorithm and a new mesh simplification algorithm. Both algorithms have a preprocessing step and are followed by the remeshing or mesh simplification steps. The preprocessing step computes an importance map that indicates the visual masking potential of the visual patterns on the surface. The importance map is then used to guide the remeshing or mesh simplification algorithms. Two different methods are proposed for computing an importance map that indicates the masking potential of the visual patterns on the surface. The first one is based on the Sarnoff visual discrimination metric, and the second one is inspired by the visual masking tool available in the current JPEG2000 standard. Given an importance map, the surface remeshing algorithm automatically distributes few samples to surface regions with strong visual masking properties due to surface texturing, lighting variations, bump mapping, surface reflectance, and interreflections. Similarly, the mesh simplification algorithm simplifies more aggressively where the light field of an object can hide more geometric artifacts.",

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note = "Funding Information: The authors would like to thank all anonymous reviewers for their helpful comments and suggestions, which have significantly improved this paper. They also want to thank Michael Garland for making his QSlim software available, and Cyberware and Stanford Graphics Lab for making the Igea and Bunny models available. The assistance of Jon Konieczny in running timing tests is also gratefully acknowledged. Funding was provided by the National Science Foundation Grant CCR-0242757. This research was performed at the University of Minnesota Digital Technology Center.",

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Perceptually guided polygon reduction

Abstract

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Keywords

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