loading
  • Login
  • Sign-Up

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Nadejda Roubtsova and Jean-Yves Guillemaut

Affiliation: University of Surrey, United Kingdom

ISBN: 978-989-758-009-3

Keyword(s): 3D Reconstruction, Helmholtz Stereopsis, Complex Reflectance.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image-Based Modeling ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Software Engineering ; Stereo Vision and Structure from Motion

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. Furthermore, 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 reformulating 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 quali tative 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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SCITEPRESS user or Join INSTICC now for free.

Sign In SCITEPRESS user: please login.

Sign In INSTICC Members: please login. If not a member yet, Join INSTICC now for free.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 23.20.218.77. INSTICC members have higher download limits (free membership now)

In the current month:
Recent papers: 1 available of 1 total
2+ years older papers: 2 available of 2 total

Paper citation in several formats:
Roubtsova N. and Guillemaut J. (2014). A Bayesian Framework for Enhanced Geometric Reconstruction of Complex Objects by Helmholtz Stereopsis.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014)ISBN 978-989-758-009-3, pages 335-342. DOI: 10.5220/0004683503350342

@conference{visapp14,
author={Nadejda Roubtsova and Jean-Yves Guillemaut},
title={A Bayesian Framework for Enhanced Geometric Reconstruction of Complex Objects by Helmholtz Stereopsis},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014)},
year={2014},
pages={335-342},
doi={10.5220/0004683503350342},
isbn={978-989-758-009-3},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014)
TI - A Bayesian Framework for Enhanced Geometric Reconstruction of Complex Objects by Helmholtz Stereopsis
SN - 978-989-758-009-3
AU - Roubtsova N.
AU - Guillemaut J.
PY - 2014
SP - 335
EP - 342
DO - 10.5220/0004683503350342

Sorted by: Show papers

Note: The preferred Subjects/Areas/Topics, listed below for each paper, are those that match the selected paper topics and their ontology superclasses.
More...

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.

Show authors

Note: The preferred Subjects/Areas/Topics, listed below for each author, are those that more frequently used in the author's papers.
More...