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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.

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Abstract

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 :
NameEmailORCID
Roubtsova, Nadejdan.s.roubtsova@surrey.ac.ukUNSPECIFIED
Guillemaut, Jean-YvesJ.Guillemaut@surrey.ac.ukUNSPECIFIED
Date : 2017
Copyright Disclaimer : © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Uncontrolled Keywords : Helmholtz Stereopsis; 3D; Complex reflectance; MAP
Depositing User : Clive Harris
Date Deposited : 05 Sep 2017 13:32
Last Modified : 05 Sep 2017 13:32
URI: http://epubs.surrey.ac.uk/id/eprint/842176

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