A Theory of Photometric Stereo for a Class of Diffuse Non-Lambertian Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Beyond Lambert: Reconstructing Specular Surfaces Using Color
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
An Algebraic Approach to Surface Reconstruction from Gradient Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Shape and Spatially-Varying BRDFs from Photometric Stereo
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Quasiconvex Optimization for Robust Geometric Reconstruction
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
Automatic Estimation and Removal of Noise from a Single Image
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
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Feasibility and Infeasibility in Optimization: Algorithms and Computational Methods
Shape and materials by example: a photometric stereo approach
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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
Dense photometric stereo by expectation maximization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust 3D face capture using example-based photometric stereo
Computers in Industry
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We present a Lambertian photometric stereo algorithm robust to specularities and shadows and it is based on a maximum feasible subsystem (Max FS) framework. A Big-M method is developed to obtain the maximum subset of images that satisfy the Lambertian constraint among the whole set of captured photometric stereo images which include non-Lambertian reflections such as specularities and shadows. Our algorithm employs purely algebraic pixel-wise optimization without relying on probabilistic/physical reasoning or initialization, and it guarantees the global optimality. It can be applied to the image sets with the number of images ranging from four to hundreds, and we show that the computation time is reasonably short for a medium number of images (10-100). Experiments are carried out with various objects to demonstrate the effectiveness of the algorithm.