Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs
Abstract
The uncalibrated photometric stereo problem for non-Lambertian surfaces is challenging because of the large number of unknowns and its ill-posed nature stemming from unknown reflectance functions. We propose a model that represents various isotropic reflectance functions by using the principal components of items in a dataset, and formulate the uncalibrated photometric stereo as a regression problem. We then solve it by stepwise optimization utilizing principal components in order of their importance. We have also developed two techniques that lead to convergence and highly accurate reconstruction, namely (1) a coarse-to-fine approach with normal grouping, and (2) a randomized multipoint search. Our experimental results with synthetic data showed that our method significantly outperformed previous methods. We also evaluated the algorithm in terms of real image data, where it gave good reconstruction results.
Cite
Text
Midorikawa et al. "Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.471Markdown
[Midorikawa et al. "Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/midorikawa2016cvpr-uncalibrated/) doi:10.1109/CVPR.2016.471BibTeX
@inproceedings{midorikawa2016cvpr-uncalibrated,
title = {{Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs}},
author = {Midorikawa, Keisuke and Yamasaki, Toshihiko and Aizawa, Kiyoharu},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2016},
doi = {10.1109/CVPR.2016.471},
url = {https://mlanthology.org/cvpr/2016/midorikawa2016cvpr-uncalibrated/}
}