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Photometric Segmentation: Simultaneous Photometric Stereo and Masking

Bjoern Haefner 1 Yvain Quéau 2 Daniel Cremers 1
2 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : This work is concerned with both the 3D-reconstruction of an object using photometric stereo, and its 2D-segmentation from the background. In contrast with previous works on photometric stereo which assume that a mask of the area of interest has been computed beforehand, we formulate 3D-reconstruction and 2D-segmentation as a joint problem. The proposed variational solution combines a differential formulation of photometric stereo with the classic Chan-Vese model for active contours. Given a set of photometric stereo images, this solution simultaneously infers a binary mask of the object of interest and a depth map representing its 3D-shape. Experiments on real-world datasets confirm the soundness of simultaneously solving both these classic computer vision problems, as the joint approach considerably simplifies the overall 3D-scanning process for the end-user.
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Bjoern Haefner, Yvain Quéau, Daniel Cremers. Photometric Segmentation: Simultaneous Photometric Stereo and Masking. International Conference on 3D Vision (3DV 2019), Sep 2019, Québec, Canada. ⟨10.1109/3DV.2019.00033⟩. ⟨hal-02266352⟩

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