A Theory of Photometric Stereo for a Class of Diffuse Non-Lambertian Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric computation for machine vision
Geometric computation for machine vision
Estimating the parameters of an illumination model using photometric stereo
Graphical Models and Image Processing
Extracting the Shape and Roughness of Specular Lobe Objects Using Four Light Photometric Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Surface curvature and shape reconstruction from unknown multiple illumination and integrability
Computer Vision and Image Understanding - Special issue on physics-based modeling and reasoning in computer vision
On Photometric Issues in 3D Visual Recognition from aSingle 2D Image
International Journal of Computer Vision
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
Computer Vision
Lambertian Reflectance and Linear Subspaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Median Photometric Stereo as Applied to the Segonko Tumulus and Museum Objects
International Journal of Computer Vision
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Many previous works on photometric stereo have shown how to recover the shape and reflectance properties of an object using multiple images taken under a fixed viewpoint and variable lighting conditions. However, most of them only dealt with a single point light source in each image. In this paper, we show how to perform photometric stereo with four images which are taken under distant but general lighting conditions. Our method is based on the representation that uses low-order spherical harmonics for Lambertian objects. Attached shadows are considered in this representation. We show that the lighting conditions can be estimated regardless of object shape and reflectance properties. The estimated illumination conditions can then help to recover the shape and reflectance properties.