Structure from motion and photometric stereo for dense 3D shape recovery

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Abstract

In this paper we present a dense 3D reconstruction pipeline from monocular video sequences using jointly Photometric Stereo (PS) and Structure from Motion (SfM) approaches. The input videos are completely uncalibrated both from the multi-view geometry and photometric stereo aspects. In particular we make use of the 3D metric information computed with SfM from a set of 2D landmarks in order to solve for the bas-relief ambiguity which is intrinsic from dense PS surface estimation. The algorithm is evaluated over the CMU Multi-Pie database which contains the images of 337 subjects viewed under different lighting conditions and showing various facial expressions. © 2011 Springer-Verlag.

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APA

Sabzevari, R., Del Bue, A., & Murino, V. (2011). Structure from motion and photometric stereo for dense 3D shape recovery. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6978 LNCS, pp. 660–669). https://doi.org/10.1007/978-3-642-24085-0_67

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