Conference Paper

Robust Photometric Stereo via Low-Rank Matrix Completion and Recovery.

DOI: 10.1007/978-3-642-19318-7_55 Conference: Computer Vision - ACCV 2010 - 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part III
Source: DBLP

ABSTRACT We present a new approach to robustly solve photometric stereo problems. We cast the problem of recovering surface normals from multiple lighting conditions as a problem of recovering a low-rank matrix with both missing entries and corrupted entries, which model all types of non-Lambertian eects such as shadows and specularities. Unlike previ- ous approaches that use Least-Squares or heuristic robust techniques, our method uses advanced convex optimization techniques that are guaranteed to nd the correct low-rank matrix by simultaneously xing its missing and erroneous entries. Extensive experimental results demonstrate that our method achieves unprecedentedly accurate estimates of surface nor- mals in the presence of signicant amount of shadows and specularities. The new technique can be used to improve virtually any photometric stereo method including uncalibrated photometric stereo.

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