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International Journal of Computer Vision
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
International Journal of Computer Vision
Lambertian Reflectance and Linear Subspaces
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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
Reflections on the Generalized Bas-Relief Ambiguity
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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
Dense Photometric Stereo: A Markov Random Field Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photometric Stereo with General, Unknown Lighting
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A photometric approach for estimating normals and tangents
ACM SIGGRAPH Asia 2008 papers
Shadows in Three-Source Photometric Stereo
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Photometric Stereo via Expectation Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion segmentation with missing data using power factorization and GPCA
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust estimation of surface properties and interpolation of shadow/specularity components
Image and Vision Computing
The scale of geometric texture
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
International Journal of Computer Vision
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Photometric stereo relies on inverting the image formation process, and doing this accurately requires reasoning about the visibility of light sources with respect to each image point. While simple heuristics for shadow detection suffice in some cases, they are susceptible to error. This paper presents an alternative approach for handling visibility in photometric stereo, one that is suitable for uncalibrated settings where the light directions are not known. A surface imaged under a finite set of light sources can be divided into regions having uniform visibility, and when the surface is Lambertian, these regions generally map to distinct three-dimensional illumination subspaces. We show that by identifying these subspaces, we can locate the regions and their visibilities, and in the process identify shadows. The result is an automatic method for uncalibrated Lambertian photometric stereo in the presence of shadows, both cast and attached.