Robust Photometric Stereo using Sparse Regression

Satoshi Ikehata, David Wipf, Yasuyuki Matsushita, and Kiyoharu Aizawa


This paper presents a robust photometric stereo method that effectively compensates for various non-Lambertian corruptions such as specularities, shadows, and image noise. We construct a constrained sparse regression problem that enforces both Lambertian, rank-3 structure and sparse, additive corruptions. A solution method is derived using a hierarchical Bayesian approximation to accurately estimate the surface normals while simultaneously separating the non-Lambertian corruptions. Extensive evaluations are performed that show state-of-the-art performance using both synthetic and real-world images.


Publication typeProceedings
PublisherIEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
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