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Analyzing Time-Varying Surface Appearance with TVBRDF Models

Explore the efficient acquisition and analysis of time-varying BRDFs to model changing material appearances over time. Develop analytic TVBRDF models for paints, wet surfaces, and dust. Discuss challenges, system setup, data fitting, and future directions.

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Analyzing Time-Varying Surface Appearance with TVBRDF Models

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  1. Time-Varying BRDFs Bo Sun Kalyan Sunkavalli Ravi Ramamoorthi Peter Belhumeur Shree Nayar Columbia University

  2. Materials Change with Time

  3. Previous Work • Time-Varying Spatial Albedo Patterns • Paints, Wet Surfaces and Dust [J. Dorsey et al., 96] [J. Dorsey et al., 99] [J. Lu et al., 05] [S. Enrique et al., 05] [C. J. Curtis., 97] [J. W. Jensen et al., 99] [E. Nakamae et al., 96] [S. Hsu et al., 96]

  4. Previous Work Time-Varying Surface Appearance [J. Gu et al. 2006] Appearance Manifolds [J. Wang et al. 2006]

  5. Our Goals • Efficient TVBRDF Acquisition • Underlying Temporal Trend Analysis • Developing Analytic TVBRDF Models

  6. Acquisition Challenges • A Key Consideration: Fine sampling in the time domain • High frequency reflectance in the angular domain

  7. Acquisition System

  8. Acquisition System

  9. Acquisition System

  10. Acquisition System

  11. Acquisition System

  12. Sample Plate before after desired

  13. Acquisition System 12 seconds / scan, 360 color images / camera, 0.2~32 millisec exposures

  14. Acquisition - Sampling

  15. TVBRDF Samples 41 samples and three time-varying effects

  16. TVBRDF Samples 41 samples and three time-varying effects

  17. TVBRDF Samples 41 samples and three time-varying effects

  18. TVBRDF Samples 41 samples and three time-varying effects

  19. TVBRDF Samples 41 samples and three time-varying effects

  20. Measurement Top Camera Top Camera Top Camera

  21. Analytic BRDF Functions • The Oren-Nayar diffuse model • The Torrance-Sparrow specular model • The Blinn’s dust model

  22. Data Fitting Top Camera Maximum RMS Error 3.84% Top Camera Top Camera

  23. TVBRDF Database

  24. TVBRDF Database

  25. TVBRDF Database

  26. TVBRDF Database

  27. Temporal Trends

  28. Time-Varying Phenomena • Paints drying on smooth surfaces • Water drying on rough surfaces • Dust accumulation

  29. Paints • Exponential fall-off of specular albedo and roughness • Diffuse color shifts in a dichromatic plane

  30. Paints • Exponential fall-off of specular albedo and roughtness • Diffuse color shifts in a dichromatic plane

  31. Paints • Exponential fall-off of specular albedo and roughtness • Diffuse color shifts in a dichromatic plane

  32. Paints • Exponential fall-off of specular albedo and roughtness • Diffuse color shifts in a dichromatic plane

  33. Paints – Spatial Variations Captured Blue Watercolor on White Surface

  34. Paints – Effects Transfer Synthesized Green Watercolor on White Surface

  35. Paints – Effects Transfer Synthesized Blue Watercolor on Red Surface

  36. Wet Surfaces • Diffuse color shifts on a straight line • Sigmoidal change of surface intensity

  37. Wet Surfaces • Diffuse color shifts on a straight line • Sigmoidal change of surface intensity

  38. Wet Surfaces • Diffuse color shifts on a straight line • Sigmoidal change of surface intensity

  39. Time-Varying Phenomena • Paints drying on smooth surfaces • Water drying on rough surfaces • Dust accumulation

  40. Dust • Analysis generalizes to other BRDF models • Exponential fall-off of specular highlights

  41. Dust • Analysis generalizes to other BRDF models • Exponential fall-off of specular highlights

  42. Dust – Final Example

  43. Summary • Acquisition of TVBRDF database (HDR) • Analysis of temporal parameter trends • Analytic TVBRDF models

  44. Future Work • Using more complicated BRDF models • Generalize to other time-vayring phenomena • Time–varying BTF, subsurface scattering

  45. Thanks for Listening! http://www.cs.columbia.edu/~bosun/research

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