Photometric stereo with non-parametric and spatially-varying reflectance

We present a method for simultaneously recovering shape and spatially varying reflectance of a surface from photometric stereo images. The distinguishing feature of our approach is its generality; it does not rely on a specific parametric reflectance model and is therefore purely "datadriven". This is achieved by employing novel bi-variate approximations of isotropic reflectance functions. By combining this new approximation with recent developments in photometric stereo, we are able to simultaneously estimate an independent surface normal at each point, a global set of non-parametric "basis material" BRDFs, and per-point material weights. Our experimental results validate the approach and demonstrate the utility of bi-variate reflectance functions for general non-parametric appearance capture.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Neil G. Alldrin, Todd Zickler, David J. Kriegman
• Keywords: Bi-variate Reflectance Functions, Computer Vision, CVPR 2008, Isotropic Reflectance Functions, Non-parametric Appearance Capture, Photometric Stereo Images, Specific Parametric Reflectance