A Theory of Specular Surface Geometry
International Journal of Computer Vision
International Journal of Computer Vision
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction
International Journal of Computer Vision
Specularities Reduce Ambiguity of Uncalibrated Photometric Stereo
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Shape and albedo from multiple images using integrability
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Incorporating the Torrance and Sparrow Model of Reflectance in Uncalibrated Photometric Stereo
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Dense Photometric Stereo Using a Mirror Sphere and Graph Cut
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Reflections on the Generalized Bas-Relief Ambiguity
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Example-Based Photometric Stereo: Shape Reconstruction with General, Varying BRDFs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Can Two Specular Pixels Calibrate Photometric Stereo?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Clustering Appearance for Scene Analysis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Retrieving multiple light sources in the presence of specular reflections and texture
Computer Vision and Image Understanding
Visibility subspaces: uncalibrated photometric stereo with shadows
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Robust photometric stereo via low-rank matrix completion and recovery
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
What is the range of surface reconstructions from a gradient field?
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A closed-form solution to uncalibrated photometric stereo via diffuse maxima
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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Images of an object under different illumination are known to provide strong cues about the object surface. A mathematical formalization of how to recover the normal map of such a surface leads to the so-called uncalibrated photometric stereo problem. In the simplest instance, this problem can be reduced to the task of identifying only three parameters: the so-called generalized bas-relief (GBR) ambiguity. The challenge is to find additional general assumptions about the object, that identify these parameters uniquely. Current approaches are not consistent, i.e., they provide different solutions when run multiple times on the same data. To address this limitation, we propose exploiting local diffuse reflectance (LDR) maxima, i.e., points in the scene where the normal vector is parallel to the illumination direction (see Fig. 1). We demonstrate several noteworthy properties of these maxima: a closed-form solution, computational efficiency and GBR consistency. An LDR maximum yields a simple closed-form solution corresponding to a semi-circle in the GBR parameters space (see Fig. 2); because as few as two diffuse maxima in different images identify a unique solution, the identification of the GBR parameters can be achieved very efficiently; finally, the algorithm is consistent as it always returns the same solution given the same data. Our algorithm is also remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80 % of the observations). The method is validated on real data and achieves state-of-the-art results.