Shape from Video: Dense Shape, Texture, Motion and Lighting from Monocular Image Streams

Abstract : This paper presents a probabilistic framework for robust recovery of dense 3D shape, motion, texture and lighting from monocular image streams. We assume that the object is smooth, Lambertian, illuminated by one distant light source, and subject to smoothly-varying rigid motion. The problem is formulated as a MAP estimation problem in which all shape, motion, noise variances and outlier probabilities are estimated simultaneously. Estimation is performed using a multi-stage initialization process followed by a large-scale quasi-Newtonian optimization technique.
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Peter Belhumeur and Katsushi Ikeuchi and Emmanuel Prados and Stefano Soatto and Peter Sturm. Proceedings of the First International Workshop on Photometric Analysis For Computer Vision - PACV 2007, Oct 2007, Rio de Janeiro, Brazil. INRIA, 8 p., 2007
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Azeem Lakdawalla, Aaron Hertzmann. Shape from Video: Dense Shape, Texture, Motion and Lighting from Monocular Image Streams. Peter Belhumeur and Katsushi Ikeuchi and Emmanuel Prados and Stefano Soatto and Peter Sturm. Proceedings of the First International Workshop on Photometric Analysis For Computer Vision - PACV 2007, Oct 2007, Rio de Janeiro, Brazil. INRIA, 8 p., 2007. 〈inria-00264915〉

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