Abstract:

We present a method for simultaneously recovering shape and spatially varying re?ectance of a surface from photometric stereo images. The distinguishing feature of our approach is its generality; it does not rely on a speci?c parametric re?ectance model and is therefore purely "data-driven". This is achieved by employing novel bi-variate approximations of isotropic re?ectance 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 re?ectance functions for general non-parametric appearance capture.

For datasets and additional stunning results see Neil Alldrin's project page.

References:

• Neil Alldrin, Todd Zickler and David Kriegman, "Photometric Stereo with Non-parametric and Spatially-varying Reflectance." Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2008. [PDF]

Some of this material is based upon work supported by the National Science Foundation under Grant No. 0541173. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Updated: Dec 23, 2008