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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 Nathaniel Williams UNC David Luebke UVA Jonathan D. Cohen JHU Michael Kelley UVA Brenden Schubert UVA
Motivation: large datasets Scanning Monticello Project In 10 hours we collected 185,000,000 point samples with a scanning laser rangefinder
Solution: level of detail • Simplify complex models to achieve interactivity • 25+ years of active research [Clark 1976]
The key issues • How should we simplify the data? • How should we regulate the level of detail? • How should we evaluate the results?
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
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
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
Perceptual model:The contrast sensitivity function • Model is based on contrast gratings Contrast Courtesy of Izumi Ohzawa Spatial Frequency (cycles/degree)
Perceptual model:The contrast sensitivity function • Predicts the threshold perceptibility of a stimulus given its size and contrast Figure courtesy of Martin Reddy
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
Ymin Ymax Maximum Michelson contrast
r Ф Minimum spatial frequency
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
Texture deviation • Improved bound on the size of features altered by simplification
The Multi-Triangulation • Directed acyclic graph • Nodes • Edge collapse operations • Arcs • Node dependencies • Mesh triangles • Triangles are explicitly represented • Good for preprocessing
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
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
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
Diffuse Specular Illumination contrast
Demonstration • Show Video
Evaluation • Perceptually motivated image metric • ltdiff [Lindstrom 2000] • Comparison to a Multi-Triangulation based implementation of Appearance Preserving Simplification • Cohen et al. 1998
Results 500,000 triangle armadillo with per-vertex normals
Error High Low Results: 98% simplified Screen-space Error: 3,689 Perceptually guided Error: 3,123
Results: memory usage 500,000 triangle armadillo
Discussion: Pros • Unified framework for interactive rendering • Based on perceptual metric (CSF) • Sensitive to texture, illumination, and silhouettes • Parameter-free • No tweaking required!
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
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
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