GVI Group  Harvard University

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 "data driven". 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.

For more information, see Neil Alldrin's research page at UCSD.

@proceedings { 179, title = {Photometric Stereo with Non-parametric and Spatially-varying Reflectance}, year = {2008}, author = {Neil Alldrin and Todd Zickler and David Kriegman} }