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Bayesian Helmholtz Stereopsis with Integrability Prior

Roubtsova, Nadejda and Guillemaut, Jean-Yves (2017) Bayesian Helmholtz Stereopsis with Integrability Prior IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (9). pp. 2265-2272.

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Helmholtz Stereopsis is a 3D reconstruction method uniquely independent of surface reflectance. Yet, its sub-optimal maximum likelihood formulation with drift-prone normal integration limits performance. Via three contributions this paper presents a complete novel pipeline for Helmholtz Stereopsis. Firstly, we propose a Bayesian formulation replacing the maximum likelihood problem by a maximum a posteriori one. Secondly, a tailored prior enforcing consistency between depth and normal estimates via a novel metric related to optimal surface integrability is proposed. Thirdly, explicit surface integration is eliminated by taking advantage of the accuracy of prior and high resolution of the coarse-to-fine approach. The pipeline is validated quantitatively and qualitatively against alternative formulations, reaching sub-millimetre accuracy and coping with complex geometry and reflectance.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Date : 22 September 2017
Funders : Engineering and Physical Sciences Research Council (EPSRC)
DOI : 10.1109/TPAMI.2017.2749373
Copyright Disclaimer : This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see
Uncontrolled Keywords : Helmholtz Stereopsis; 3D; Complex reflectance; MAP
Related URLs :
Additional Information : Our synthetic data is available at Source of 3D models used (pear/bunny): Suggestive Contour model database, and Stanford 3D scanning repository, respectively.
Depositing User : Clive Harris
Date Deposited : 05 Sep 2017 13:32
Last Modified : 11 Dec 2018 11:23

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