@inproceedings{ikehata2012robust, author = {Ikehata, Satoshi and Wipf, David and Matsushita, Yasuyuki and Aizawa, Kiyoharu}, title = {Robust Photometric Stereo using Sparse Regression}, booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2012}, month = {January}, abstract = {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.}, url = {http://approjects.co.za/?big=en-us/research/publication/robust-photometric-stereo-using-sparse-regression/}, }