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Efficient Acquisition and Realistic Rendering of Car Paint

Efficient Acquisition and Realistic Rendering of Car Paint. Johannes Günther, Tongbo Chen, Michael Goesele, Ingo Wald, and Hans-Peter Seidel MPI Informatik Saarbrücken, Germany. Motivation. Virtual prototyping, car design by computer Mainly two materials

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Efficient Acquisition and Realistic Rendering of Car Paint

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  1. Efficient Acquisition andRealistic Rendering of Car Paint Johannes Günther, Tongbo Chen, Michael Goesele, Ingo Wald, and Hans-Peter Seidel MPI Informatik Saarbrücken, 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 VMV, Erlangen, Germany

  3. Outline • Introduction & Previous Work • Efficient Acquisition • Measurement Setup • BRDF Representation and Modelling • Realistic Rendering • BRDF Evaluation • Illumination • Simulation of Sparkling • Results • Conclusion & Future Work VMV, Erlangen, Germany

  4. 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] VMV, Erlangen, Germany

  5. Outline • Introduction & Previous Work • Efficient Acquisition • Realistic Rendering • Results • Conclusion & Future Work VMV, Erlangen, Germany

  6. CCD camera white LED painted sphere turn table Measurement Setup VMV, Erlangen, Germany

  7. 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 VMV, Erlangen, Germany

  8. Targets VMV, Erlangen, Germany

  9. 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 VMV, Erlangen, Germany

  10. reflectance 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 highlight (clear coat) glitter (flakes) base color VMV, Erlangen, Germany

  11. Outline • Introduction & Previous Work • Efficient Acquisition • Realistic Rendering • Results • Conclusion & Future Work VMV, Erlangen, Germany

  12. reflectance φ Complex Illumination • HDR Environment Maps for direct illumination • Options for BRDF evaluation: • Sample Environment Map • Discretize into directional lights [Kollig ‘03, Agarwal ‘03, …] • Works well for diffuse BRDFs • Sample BRDF • Good for specular BRDFs • Decompose car paint BRDF into diffuse part and highly specular part car paint BRDF highly specular split mostly diffuse VMV, Erlangen, Germany

  13. clear coat flakes base color Sparkles • Prominent feature of metallic paints • Tiny bright spots when viewed from close distance • Caused by mirror-like flakes • Reflect light directly to eye VMV, Erlangen, Germany

  14. 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 VMV, Erlangen, Germany

  15. Outline • Introduction & Previous Work • Efficient Acquisition • Realistic Rendering • Results • Conclusion & Future Work VMV, Erlangen, Germany

  16. Model Comparison ClearCoat™ Phong BRDF table fitted BRDF VMV, Erlangen, Germany

  17. Video VMV, Erlangen, Germany

  18. Conclusion • Easy-to-build and fast acquisition system • Measured car paint and measured lighting environment for convincing car renderings • Frame-to-frame coherent sparkling simulation • Future Work • Extend car paint database • Multi-level methods for sparkles (avoid aliasing) VMV, Erlangen, Germany

  19. Data sets available • Project homepage: http://www.mpi-inf.mpg.de/~guenther/carpaint/ VMV, Erlangen, Germany

  20. Questions? • Project homepage: http://www.mpi-inf.mpg.de/~guenther/carpaint/ Thank You VMV, Erlangen, Germany

  21. VMV, Erlangen, Germany

  22. Performance vs. Quality • Cluster of 20 dual Opteron 2.5 GHz PCs • Vary parameter to tune rendering speed or quality 640 × 480 16 lights no over sampling 12.1 fps 640 × 480 128 lights 16 spp 1.3 fps 1280 × 960 1024 lights 64 spp 97 sec VMV, Erlangen, Germany

  23. Offline Rendering VMV, Erlangen, Germany

  24. The Different Car Paints VMV, Erlangen, Germany

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