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

Yvain Quéau; Tao Wu; Francois Bernard Lauze; Jean-Denis Durou; Daniel Cremers

doi:10.1109/CVPR.2017.45

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

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

Datalogisk Institut

28 Citationer (Scopus)

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.

Originalsprog	Engelsk
Titel	2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Antal sider	10
Forlag	IEEE
Publikationsdato	6 nov. 2017
Sider	350-359
ISBN (Elektronisk)	978-1-5386-0457-1
DOI	https://doi.org/10.1109/CVPR.2017.45
Status	Udgivet - 6 nov. 2017
Begivenhed	2017 IEEE Conference on Computer Vision and Pattern Recognition - Hawaii Convention Center, Honolulu, USA Varighed: 21 jul. 2017 → 26 jul. 2017

Konference

Konference	2017 IEEE Conference on Computer Vision and Pattern Recognition
Lokation	Hawaii Convention Center
Land/Område	USA
By	Honolulu
Periode	21/07/2017 → 26/07/2017

Adgang til dokumentet

10.1109/CVPR.2017.45

Citationsformater

Quéau, Y, Wu, T, Lauze, FB, Durou, J-D & Cremers, D 2017, A non-convex variational approach to photometric stereo under inaccurate lighting. i 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, s. 350-359, 2017 IEEE Conference on Computer Vision and Pattern Recognition , Honolulu, Hawaii, USA, 21/07/2017. https://doi.org/10.1109/CVPR.2017.45

@inproceedings{394140e4e62e430eb97a58eb08d08053,

title = "A non-convex variational approach to photometric stereo under inaccurate lighting",

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.",

author = "Yvain Qu{\'e}au and Tao Wu and Lauze, {Francois Bernard} and Jean-Denis Durou and Daniel Cremers",

year = "2017",

month = nov,

day = "6",

doi = "10.1109/CVPR.2017.45",

language = "English",

pages = "350--359",

booktitle = "2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",

publisher = "IEEE",

note = "2017 IEEE Conference on Computer Vision and Pattern Recognition , CVPR 2017 ; Conference date: 21-07-2017 Through 26-07-2017",

}

TY - GEN

T1 - A non-convex variational approach to photometric stereo under inaccurate lighting

AU - Quéau, Yvain

AU - Wu, Tao

AU - Lauze, Francois Bernard

AU - Durou, Jean-Denis

AU - Cremers, Daniel

PY - 2017/11/6

Y1 - 2017/11/6

N2 - 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.

AB - 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.

U2 - 10.1109/CVPR.2017.45

DO - 10.1109/CVPR.2017.45

M3 - Article in proceedings

SP - 350

EP - 359

BT - 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

PB - IEEE

T2 - 2017 IEEE Conference on Computer Vision and Pattern Recognition

Y2 - 21 July 2017 through 26 July 2017

ER -