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

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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

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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