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Reflectance Function Approximation

Reflectance Function Approximation. Ted Wild CS 766 Thursday, December 11, 2003. Motivation. Material recognition Classification Problem Dror et al. Recognizing materials with known reflectance functions CUReT, Dana et al. Example. Denim + Cotton + Skin = Person

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Reflectance Function Approximation

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  1. Reflectance Function Approximation Ted Wild CS 766 Thursday, December 11, 2003

  2. Motivation • Material recognition • Classification Problem • Dror et al. • Recognizing materials with known reflectance functions • CUReT, Dana et al.

  3. Example • Denim + Cotton + Skin = Person • Can make feature tracking, segmentation, etc. easier • Would work under different pose, lighting

  4. Reflectance • How light and surface interact • Depends on angle at which light hits surface and view angle • Figure by Wallace and Price

  5. Bidirectional Reflectance Distribution Functions • Scalar function of 4 variables: • Incident light (2 angles) • View direction (2 angles) • CUReT data • BRDF’s of 61 materials • 205 measurements per material

  6. CUReT

  7. CUReT

  8. CUReT

  9. BRDF Approximation • Kernel regression • Gaussian kernels • 2 parameters

  10. Approximation Results

  11. BRDF Classification • Given: • Set of known BRDF functions • Set of BRDF measurements for a material • Determine what material the measurements are taken from

  12. Method • Approximate known reflectance functions from data • Kernel regression • Use nearest-neighbor classification to identify new function • Evaluation: Leave out random data from CUReT measurements, try to classify left out data

  13. Questions • How accurate does reflectance function approximation have to be for classification? • How many points are needed to get sufficient accuracy? • Known BRDF approximation • Classification • What models of reflectance work well?

  14. Classification Results

  15. Problems • Need to know: • Geometry • Discussed in class • Illumination • Ramamoorthi and Hanrahan • Factorization technique to recover BRDF and lighting in some cases • Can only recognize “known” materials

  16. Recognizing Unseen Materials • If input is sufficiently different from known BRDF’s, create a new class for it • Use linear combination of known BRDF’s for further recognition • May need less points for recognition than for approximation • Can improve approximation of new class as more of its measurements are identified

  17. Very Early Results • Leave one material out: • When testing, classify material as unseen if the distance to its nearest neighbor >= tol • tol = 0.25 • Average error: 0.40, Predicting unseen: 0.51 • tol = 0.20 • Average error: 0.45, Predicting unseen: 0.29 • Trials only run once!

  18. Influences • Dror et al. (2001) • Material classification based on reflectance • Lensch et al. (2001) • Representation of BRDF’s as combination of a few basis BRDF’s. • Dana et al. (1997) • Use of CUReT data to evaluate reflectance function approximation

  19. Future Work • Complete and test method for unseen material recognition • Reduce error for approximation and classification methods • Recognition of materials under unknown geometry and/or illumination • Evaluate other reflectance models

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