Mesostructure from Specularity
Tongbo Chen1 Michael Goesele2 Hans-Peter Seidel11MPI Informatik 2University of Washington
Abstract
We describe a simple and robust method for surface mesostructure acquisition. Our method builds on the observation that specular reflection is a reliable visual cue for surface mesostructure perception. In contrast to most photometric stereo methods, which take specularities as outliers and discard them, we propose a progressive acquisition system that captures a dense specularity field as the only information for mesostructure reconstruction. Our method can efficiently recover surfaces with fine-scale geometric details from complex real-world objects with a wide variety of reflection properties, including translucent, low albedo, and highly specular objects. We show results for a variety of objects including skin, apricot, orange, jelly candy, black leather and dark chocolate.
Paper
Tongbo Chen, Michael Goesele and Hans-Peter Seidel. Mesostructure from Specularity. In proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), New York, NY, USA, June 17-22, 2006, pp. 1825-1832. PDF Project webpages