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Optimised photometric stereo via non-convex variational minimisation

Abstract : Estimating the shape and appearance of a three dimensional object from flat images is a challenging research topic that is still actively pursued. Among the various techniques available, Photometric Stereo is known to provide very accurate local shape recovery, in terms of surface normals. In this work, we propose to minimise non-convex variational models for Photometric Stereo that recover the depth information directly. We suggest an approach based on a novel optimisation scheme for non-convex cost functions. Experiments show that our strategy achieves more accurate results than competing approaches.
Keywords : Photometric stereo
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https://hal.archives-ouvertes.fr/hal-01445135
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  • HAL Id : hal-01445135, version 1
  • OATAO : 17234

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Laurent Hoeltgen, Yvain Quéau, Michael Breuss, Georg Radow. Optimised photometric stereo via non-convex variational minimisation. 27th British Machine Vision Conference (BMVC 2016), Sep 2016, York, United Kingdom. pp. 1. ⟨hal-01445135⟩

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