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Analytical results on error sensitivity of motion estimation from two views
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International Journal of Computer Vision
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A Multilinear Singular Value Decomposition
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A critique of structure-from-motion algorithms
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Structure from Motion Causally Integrated Over Time
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Lambertian Reflectance and Linear Subspaces
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Video-based face recognition using probabilistic appearance manifolds
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An information theoretic criterion for evaluating the quality of 3-D reconstructions from video
IEEE Transactions on Image Processing
Statistical bias in 3-D reconstruction from a monocular video
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Efficient tracking and ego-motion recovery using gait analysis
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ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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In this paper, we present a theory for combining the effects of motion, illumination, 3D structure, albedo, and camera parameters in a sequence of images obtained by a perspective camera. We show that the set of all Lambertian reflectance functions of a moving object, at any position, illuminated by arbitrarily distant light sources, lies "close” to a bilinear subspace consisting of nine illumination variables and six motion variables. This result implies that, given an arbitrary video sequence, it is possible to recover the 3D structure, motion, and illumination conditions simultaneously using the bilinear subspace formulation. The derivation builds upon existing work on linear subspace representations of reflectance by generalizing it to moving objects. Lighting can change slowly or suddenly, locally or globally, and can originate from a combination of point and extended sources. We experimentally compare the results of our theory with ground truth data and also provide results on real data by using video sequences of a 3D face and the entire human body with various combinations of motion and illumination directions. We also show results of our theory in estimating 3D motion and illumination model parameters from a video sequence.