Efficient Acquisition andRealistic Rendering of Car Paint

Johannes Günther, Tongbo Chen, Michael Goesele,

Ingo Wald, and Hans-Peter Seidel

MPI Informatik

Saarbrücken, Germany

November 18, 2005 VMV, Erlangen, Germany 2

Motivation

Virtual prototyping, car design by computer

Mainly two materials– Glass: ok, physical

properties well known– Car paint: not so easy

Goal:

Realistic appearance of virtual cars, close to reality Phong BRDF: “plastic” look

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Introduction & Previous Work Efficient Acquisition

– Measurement Setup– BRDF Representation and Modelling

Realistic Rendering– BRDF Evaluation– Illumination– Simulation of Sparkling

Results Conclusion & Future Work

Outline

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Previous Work

BRDF Acquisition [Marschner ‘98, Matusik ‘03]– Image based, automatic fast

Car paint [Ershov ‘01, ‘04]– Complex models, many effects– Not designed for animation context

Illumination by Environment Maps [Debevec ‘98] Realtime Ray Tracing [Wald ’01, ‘04]

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Introduction & Previous Work Efficient Acquisition Realistic Rendering Results Conclusion & Future Work

Outline

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Measurement Setup

CCD camera white LED

turn tablepainted sphere

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Measurement Process

Turn table: rotate light source 180° every 1° At each position: take HDR image

– One view direction, one light direction– Sphere: each pixel different normal

many BRDF sample at once

Time: ca. 30 minutes per target

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Targets

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Modeling

Use Cook-Torrance BRDF– physically derived (micro facets)– showed to perform well [Ngan EGSR ‘05]

Non-linear fitting Multiple lobes to account for nature of car paints

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refle

cta

nce

φ

Modeling

Use Cook-Torrance BRDF– physically derived (micro facets)– showed to perform well [Ngan EGSR ‘05]

Non-linear fitting Multiple lobes to account for nature of car paints

base color

highlight(clear coat)

glitter(flakes)

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Introduction & Previous Work Efficient Acquisition Realistic Rendering Results Conclusion & Future Work

Outline

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Complex Illumination

HDR Environment Maps for direct illumination Options for BRDF evaluation:

a) Sample Environment Map– Discretize into directional lights [Kollig ‘03, Agarwal ‘03, …]

– Works well for diffuse BRDFs

b) Sample BRDF– Good for specular BRDFs

Decompose car paint BRDF

into diffuse part and

highly specular partre

flect

an

ce

φ

split

hig

hly

sp

ecu

lar

mostly diffuse

car paint BRDF

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Sparkles

Prominent feature of metallic paints

Tiny bright spots when viewed from close distance

Caused by mirror-like flakes Reflect light directly to eye

base color

flakes

clear coat

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Modeling Flakes

Coherent sparkles during animation Model flakes explicitly (the normal)

(Integrated) sparkles appear as glitter in BRDF Derive statistical flake distribution from fitted glitter lobe

Use procedural normal map Flakes are very small

anti-aliasing by over sampling

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Introduction & Previous Work Efficient Acquisition Realistic Rendering Results Conclusion & Future Work

Outline

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Model Comparison

Phong ClearCoat™

BRDF table fitted BRDF

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Video

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Conclusion

Easy-to-build and fast acquisition systemMeasured car paint and measured lighting

environment for convincing car renderingsFrame-to-frame coherent sparkling simulation

Future Work– Extend car paint database– Multi-level methods for sparkles (avoid aliasing)

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Project homepage:http://www.mpi-inf.mpg.de/~guenther/carpaint/

Data sets available

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Project homepage:http://www.mpi-inf.mpg.de/~guenther/carpaint/

Thank You

Questions?

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Performance vs. Quality

640 × 48016 lights

no over sampling12.1 fps

640 × 480128 lights

16 spp1.3 fps

1280 × 9601024 lights

64 spp97 sec

Cluster of 20 dual Opteron 2.5 GHz PCs Vary parameter to tune rendering speed or quality

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Offline Rendering

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The Different Car Paints