A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting

Yvain Queau, Tao Wu, Francois Lauze, Jean-Denis Durou, Daniel Cremers

CVPR 2017

doi:10.1109/CVPR.2017.45 /cvpr/2017/queau2017cvpr-nonconvex/

Abstract

This paper tackles the photometric stereo problem in the presence of inaccurate lighting, obtained either by calibration or by an uncalibrated photometric stereo method. Based on a precise modeling of noise and outliers, a robust variational approach is introduced. It explicitly accounts for self-shadows, and enforces robustness to cast-shadows and specularities by resorting to redescending M-estimators. The resulting non-convex model is solved by means of a computationally efficient alternating reweighted least-squares algorithm. Since it implicitly enforces integrability, the new variational approach can refine both the intensities and the directions of the lighting.

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Text

Queau et al. "A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting." Conference on Computer Vision and Pattern Recognition, 2017. doi:10.1109/CVPR.2017.45

Markdown

[Queau et al. "A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting." Conference on Computer Vision and Pattern Recognition, 2017.](https://mlanthology.org/cvpr/2017/queau2017cvpr-nonconvex/) doi:10.1109/CVPR.2017.45

BibTeX

@inproceedings{queau2017cvpr-nonconvex,
  title     = {{A Non-Convex Variational Approach to Photometric Stereo Under Inaccurate Lighting}},
  author    = {Queau, Yvain and Wu, Tao and Lauze, Francois and Durou, Jean-Denis and Cremers, Daniel},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2017},
  doi       = {10.1109/CVPR.2017.45},
  url       = {https://mlanthology.org/cvpr/2017/queau2017cvpr-nonconvex/}
}