A non-convex variational approach to photometric stereo under inaccurate lighting

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-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 languageEnglish
Title of host publication2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Number of pages10
PublisherIEEE
Publication dateJul 2017
Pages350-359
ISBN (Electronic)978-1-5386-0457-1
DOIs
Publication statusPublished - Jul 2017
Event2017 IEEE Conference on Computer Vision and Pattern Recognition - Hawaii Convention Center, Honolulu, United States
Duration: 21 Jul 201726 Jul 2017

Conference

Conference2017 IEEE Conference on Computer Vision and Pattern Recognition
LocationHawaii Convention Center
LandUnited States
ByHonolulu
Periode21/07/201726/07/2017

ID: 183735717