A non-convex variational approach to photometric stereo under inaccurate lighting
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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.
Original language | English |
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Title of host publication | 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
Number of pages | 10 |
Publisher | IEEE |
Publication date | Jul 2017 |
Pages | 350-359 |
ISBN (Electronic) | 978-1-5386-0457-1 |
DOIs | |
Publication status | Published - Jul 2017 |
Event | 2017 IEEE Conference on Computer Vision and Pattern Recognition - Hawaii Convention Center, Honolulu, United States Duration: 21 Jul 2017 → 26 Jul 2017 |
Conference
Conference | 2017 IEEE Conference on Computer Vision and Pattern Recognition |
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Location | Hawaii Convention Center |
Land | United States |
By | Honolulu |
Periode | 21/07/2017 → 26/07/2017 |
ID: 183735717