A Method for Enforcing Integrability in Shape from Shading Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calculating the reflectance map
Shape from shading
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadows in Three-Source Photometric Stereo
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Clustering appearances of objects under varying illumination conditions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Photometric sampling is a process where the surface normals of an object are estimated through the excitation of the object's surface and a rotating light source around it. The method can be regarded as a special case of photometric stereo when extensive sampling is performed in order to calculate surface normals. The classic photometric sampling approach considers only variations around the azimuth angle of the moving light source. As a consequence, additional attention has to be be paid to the recovery of the light source directions and the removal of specular and shadowed regions. This paper investigates the effect of including variations around the zenith angle of the light source vector in a photometric sampling framework, developing a geometric approach to estimate the surface normal vectors. Experiments show that increasing the number of samples along the zenith variation benefits the estimation of the surface normals.