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 Can make feature tracking, segmentation, etc. easier Would work under different pose, lighting

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

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

CUReT

CUReT

CUReT

BRDF Approximation Kernel regression Gaussian kernels 2 parameters

Approximation Results

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

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

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?

Classification Results

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

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

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!

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

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