Optimised photometric stereo via non-convex variational minimisation

Laurent Hoeltgen, Yvain Queau, Michael Breuß and Georg Radow

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.

Session

Posters 1

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DOI

10.5244/C.30.36
https://dx.doi.org/10.5244/C.30.36

Citation

Laurent Hoeltgen, Yvain Queau, Michael Breuß and Georg Radow. Optimised photometric stereo via non-convex variational minimisation. In Richard C. Wilson, Edwin R. Hancock and William A. P. Smith, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 36.1-36.12. BMVA Press, September 2016.

Bibtex

        @inproceedings{BMVC2016_36,
        	title={Optimised photometric stereo via non-convex variational minimisation},
        	author={Laurent Hoeltgen, Yvain Queau, Michael Breu{\ss} and Georg Radow},
        	year={2016},
        	month={September},
        	pages={36.1-36.12},
        	articleno={36},
        	numpages={12},
        	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
        	publisher={BMVA Press},
        	editor={Richard C. Wilson, Edwin R. Hancock and William A. P. Smith},
        	doi={10.5244/C.30.36},
        	isbn={1-901725-59-6},
        	url={https://dx.doi.org/10.5244/C.30.36}
        }