Abstract

We present a new algorithm for best-effort simplification of polygonal meshes based on principles of visual perception. Building on previous work, we use a simple model of low-level human vision to estimate the perceptibility of local simplification operations in a view-dependent Multi-Triangulation structure. Our algorithm improves on prior perceptual simplification approaches by accounting for textured models and dynamic lighting effects. We also model more accurately the scale of visual changes resulting from simplification, using parametric texture deviation to bound the size (represented as spatial frequency) of features destroyed, created, or altered by simplifying the mesh. The resulting algorithm displays many desirable properties: it is view-dependent, sensitive to silhouettes, sensitive to underlying texture content, and sensitive to illumination (for example, preserving detail near highlight and shadow boundaries, while aggressively simplifying washed-out regions). Using a unified perceptual model to evaluate these effects automatically accounts for their relative importance and balances between them, overcoming the need for ad hoc or hand-tuned heuristics.

Keywords:
Polygon mesh Heuristics Computer science Parametric statistics Triangulation Artificial intelligence Perception Computer vision Shadow (psychology) Algorithm Scale (ratio) Simple (philosophy) Texture synthesis Texture (cosmology) Pattern recognition (psychology) Image (mathematics) Image texture Mathematics Computer graphics (images) Image processing Geometry

Metrics

8
Cited By
0.71
FWCI (Field Weighted Citation Impact)
0
Refs
0.78
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Computer Graphics and Visualization Techniques
Physical Sciences →  Computer Science →  Computer Graphics and Computer-Aided Design
Visual Attention and Saliency Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Data Visualization and Analytics
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
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