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: Can only recognize “known” materials 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: tol = 0.25 tol = 0.20
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) Lensch et al. (2001) Dana et al. (1997)
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