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Extended Bayesian Helmholtz Stereopsis for Enhanced Geometric Reconstruction of Complex Objects

  • Nadejda Roubtsova
  • Jean-Yves Guillemaut
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 550)

Abstract

Helmholtz stereopsis is an advanced 3D reconstruction technique for objects with arbitrary reflectance properties that uniquely characterises surface points by both depth and normal. Traditionally, in Helmholtz stereopsis consistency of depth and normal estimates is assumed rather than explicitly enforced. Further, conventional Helmholtz stereopsis performs maximum likelihood depth estimation without neighbourhood consideration. In this paper, we demonstrate that reconstruction accuracy of Helmholtz stereopsis can be greatly enhanced by formulating depth estimation as a Bayesian maximum a posteriori probability problem. In re-formulating the problem we introduce neighbourhood support by formulating and comparing three priors: a depth-based, a normal-based and a novel depth-normal consistency enforcing one. Relative performance evaluation of the three priors against standard maximum likelihood Helmholtz stereopsis is performed on both real and synthetic data to facilitate both qualitative and quantitative assessment of reconstruction accuracy. Observed superior performance of our depth-normal consistency prior indicates a previously unexplored advantage in joint optimisation of depth and normal estimates. Further, we highlight several known artefacts of Helmholtz stereopsis due to sensor saturations, normal corruption by 2D texture and by intensity sampling at grazing angles and enrich the initially proposed pipeline of Bayesian Helmholtz stereopsis with simple yet effective extensions to tackle the artefacts.

Keywords

3D reconstruction Helmholtz stereopsis Complex reflectance 

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  1. 1.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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