perceptually guided simplification of lit, textured meshes
DESCRIPTION
Perceptually Guided Simplification of Lit, Textured Meshes. Nathaniel WilliamsUNC David LuebkeUVA Jonathan D. CohenJHU Michael KelleyUVA Brenden SchubertUVA. Motivation: large datasets. Scanning Monticello Project. - PowerPoint PPT PresentationTRANSCRIPT
UVA / UNC / JHU
Perceptually Guided Simplification of Lit, Textured Meshes
Nathaniel Williams UNCDavid Luebke UVAJonathan D. Cohen JHUMichael Kelley UVABrenden Schubert UVA
UVA / UNC / JHU
Motivation: large datasets
Scanning Monticello Project
In 10 hours we collected 185,000,000 point samples with a scanning laser rangefinder
UVA / UNC / JHU
Solution: level of detail
• Simplify complex models to achieve interactivity
• 25+ years of active research [Clark 1976]
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The key issues
• How should we simplify the data?
• How should we regulate the level of detail?
• How should we evaluate the results?
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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
UVA / UNC / JHU
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
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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
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Perceptual model:The contrast sensitivity function
• Model is based on contrast gratings
Spatial Frequency (cycles/degree)
Con
trast
Courtesy of Izumi Ohzawa
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Perceptual model:The contrast sensitivity function
• Predicts the threshold perceptibility of a stimulus given its size and contrast
Figure courtesy
of Martin Reddy
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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
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Maximum Michelson contrast
minmax
minmaxmax YY
YYC
Ymin
Ymax
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Minimum spatial frequency
Ф
r
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Texture deviation
• Distance between corresponding 3D points through P
mesh Mi mesh Mi+1
2D texture domain
(i+1)st edge collapse
XXii XXi+1i+1
xxP
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Texture deviation
• Improved bound on the size of features altered by simplification
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The Multi-Triangulation
• Directed acyclic graph♦ Nodes
• Edge collapse operations
♦ Arcs• Node dependencies• Mesh triangles
• Triangles are explicitly represented♦ Good for preprocessing
D
S
c u t
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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
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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
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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
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Illumination contrast
Diffuse Specular
nsdda HNLNTkTkY )()(
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Demonstration
• Show Video
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Evaluation
• Perceptually motivated image metric♦ ltdiff [Lindstrom 2000]
• Comparison to a Multi-Triangulation based implementation of Appearance Preserving Simplification♦ Cohen et al. 1998
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Results500,000 triangle armadillo with per-vertex normals
0500100015002000250030003500400045005000
1 2 4 8 16 32 64
Degree of Simplification:Percentage of Original Model
Ltdi
ff E
rror
View-independentScreen-spacePerceptually guidedScreen-space with tweaks
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Results: 98% simplified
Screen-space
Error: 3,689
Perceptually guided
Error: 3,123
Error
Low
High
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Results: memory usage
500,000 triangle armadillo
Memory
Original model 13.6 MB
Multi-Triangulation
66.3 MB
Perceptually Guided
74.9 MB
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Discussion: Pros
• Unified framework for interactive rendering♦ Based on perceptual metric (CSF)♦ Sensitive to texture, illumination, and
silhouettes♦ Parameter-free
• No tweaking required!
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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
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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
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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
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The End