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Two-source surface reconstruction using polarisation

Atkinson, Gary A.; Atkinson, Gary

Authors

Gary A. Atkinson



Abstract

© Springer International Publishing AG 2017. Polarisation vision aims to capture and interpret the polarisation state of incoming light as part of a computer vision system. In this paper, polarisation information is fused with two-source photometric stereo (PS) in order to determine the three-dimensional geometry of surfaces in the presence of both specular and diffuse reflection. In addition to the primary benefit of applying to both reflection types, the method lessens the effects of the Lambertian assumption, which is prevalent in many PS methods other than those requiring many light sources. Further, the method overcomes inherent ambiguities that are present in basic polarisation vision systems. The proposed method commences by using PS to deduce a constrained mapping of the surface normal at each point onto a 2D plane. Phase information from polarisation is used to deduce a mapping onto a different plane. The paper then shows how the full surface normal can be obtained from the two mappings. The results section of the paper demonstrates strong performance of the novel approach against the baseline methods for a range of real-world objects.

Citation

Atkinson, G. A., & Atkinson, G. (2017). Two-source surface reconstruction using polarisation. Lecture Notes in Artificial Intelligence, 10270, 123-135. https://doi.org/10.1007/978-3-319-59129-2_11

Acceptance Date Mar 21, 2017
Online Publication Date May 19, 2017
Publication Date Jan 1, 2017
Print ISSN 0302-9743
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 10270
Pages 123-135
Book Title Image Analysis
DOI https://doi.org/10.1007/978-3-319-59129-2_11
Keywords surface reconstruction, polarisation, photometric stereo
Public URL https://uwe-repository.worktribe.com/output/886465
Publisher URL http://dx.doi.org/10.1007/978-3-319-59129-2_11
Additional Information Title of Conference or Conference Proceedings : Scandinavian Conference on Image Analysis