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Perceptually Guided Simplification of Lit, Textured Meshes

Perceptually Guided Simplification of Lit, Textured Meshes. Nathaniel Williams UNC David Luebke UVA Jonathan D. Cohen JHU Michael Kelley UVA Brenden Schubert UVA. Motivation: large datasets. Scanning Monticello Project.

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Perceptually Guided Simplification of Lit, Textured Meshes

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  1. Perceptually Guided Simplification of Lit, Textured Meshes Nathaniel Williams UNC David Luebke UVA Jonathan D. Cohen JHU Michael Kelley UVA Brenden Schubert UVA

  2. Motivation: large datasets Scanning Monticello Project In 10 hours we collected 185,000,000 point samples with a scanning laser rangefinder

  3. Solution: level of detail • Simplify complex models to achieve interactivity • 25+ years of active research [Clark 1976]

  4. The key issues • How should we simplify the data? • How should we regulate the level of detail? • How should we evaluate the results?

  5. Our approach:Perceptually guided simplification • Regulate level of detail with a low-level model of human vision • Budget-based simplification • Unified framework for LOD selection sensitive to • Silhouettes • Texture • Dynamic lighting • No parameters to tweak

  6. Previous work:Perceptually based graphics • Human in the loop • User-guided simplification • Li & Watson 2001 • Kho & Garland 2003 • Pojar & Schmalstieg 2003 • Level of detail evaluation • Watson et al. 2001 • O’Sullivan & Dingliana 2001

  7. Previous work:Perceptually based graphics • Automatic metrics • Global illumination • Ramasubramanian et al. 1999 • LOD frequency content • Reddy 1996, 2001 • Image-driven simplification • Lindstrom & Turk 2000 • Luebke & Hallen 2001 • Focus on “imperceptible simplification” • Limited to Gouraud-shaded models with per-vertex color

  8. Perceptual model:The contrast sensitivity function • Model is based on contrast gratings Contrast Courtesy of Izumi Ohzawa Spatial Frequency (cycles/degree)

  9. Perceptual model:The contrast sensitivity function • Predicts the threshold perceptibility of a stimulus given its size and contrast Figure courtesy of Martin Reddy

  10. Perceptual model:The contrast sensitivity function • Following Luebke & Hallen 2001, we liken local simplification operations to a worst-case contrast grating • We calculate • Maximum Michelson contrast • Minimum spatial frequency

  11. Ymin Ymax Maximum Michelson contrast

  12. r Ф Minimum spatial frequency

  13. Texture deviation • Distance between corresponding 3D points through P mesh Mi mesh Mi+1 (i+1)st edge collapse Xi Xi+1 x P 2D texture domain

  14. Texture deviation • Improved bound on the size of features altered by simplification

  15. The Multi-Triangulation • Directed acyclic graph • Nodes • Edge collapse operations • Arcs • Node dependencies • Mesh triangles • Triangles are explicitly represented • Good for preprocessing

  16. Preprocessing • Augment each Multi-Triangulation node with additional information • Parametric texture deviation • Minimum bounding sphere • Texture luminance Ymin and Ymax • Normal cone for silhouette test • Normal cone for illumination test

  17. Run-time simplification • Simplification to a triangle budget • Dual-queue approach • ROAM [Duchaineau et al. 1997] • Start with cut from previous frame • Exploit temporal coherence • Calculate perceptual error of nodes given the current viewing frustum

  18. Silhouette contrast • We determine a node’s silhouette status with the normal cone • Luebke & Erikson 1997 • We conservatively assume that silhouette nodes have maximal contrast

  19. Diffuse Specular Illumination contrast

  20. Demonstration • Show Video

  21. Evaluation • Perceptually motivated image metric • ltdiff [Lindstrom 2000] • Comparison to a Multi-Triangulation based implementation of Appearance Preserving Simplification • Cohen et al. 1998

  22. Results 500,000 triangle armadillo with per-vertex normals

  23. Error High Low Results: 98% simplified Screen-space Error: 3,689 Perceptually guided Error: 3,123

  24. Results: memory usage 500,000 triangle armadillo

  25. Discussion: Pros • Unified framework for interactive rendering • Based on perceptual metric (CSF) • Sensitive to texture, illumination, and silhouettes • Parameter-free • No tweaking required!

  26. Discussion: Cons • View-dependent LOD is costly • Increased memory requirements • Higher CPU load • Less well suited for current GPUs • Summary: high fidelity, automatic simplification…for a price

  27. Future work • Improved perceptual models • Supra-threshold contrast sensitivity • Visual masking using texture content • Eccentricity & velocity • MIP-map filtering • Critical for terrain models • User studies

  28. Acknowledgements • People • Peter Lindstrom • Martin Reddy • Funding • National Science Foundation • Images and models: • Stanford 3-D Scanning Repository for the Bunny • Caltech for the Armadillo • Martin Reddy for CSF plot • Campbell-Robson Chart by Izumi Ohzawa

  29. The End

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