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Conference Paper

Mesostructure from Specularity

MPS-Authors
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Chen,  Tongbo
Computer Graphics, MPI for Informatics, Max Planck Society;

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Goesele,  Michael
Computer Graphics, MPI for Informatics, Max Planck Society;

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Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Citation

Chen, T., Goesele, M., & Seidel, H.-P. (2006). Mesostructure from Specularity. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1825-1832). Piscataway, NJ: IEEE.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-2368-C
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 human skin, dried apricot, orange, jelly candy, black leather and dark chocolate.