loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Gianmarco Addari and Jean-Yves Guillemaut

Affiliation: Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, GU2 7XH and U.K.

Keyword(s): 3D Reconstruction, Helmholtz Stereopsis, Markov Random Fields.

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

Abstract: Accurate 3D modelling of real world objects is essential in many applications such as digital film production and cultural heritage preservation. However, current modelling techniques rely on assumptions to constrain the problem, effectively limiting the categories of scenes that can be reconstructed. A common assumption is that the scene’s surface reflectance is Lambertian or known a priori. These constraints rarely hold true in practice and result in inaccurate reconstructions. Helmholtz Stereopsis (HS) addresses this limitation by introducing a reflectance agnostic modelling constraint, but prior work in this area has been predominantly limited to 2.5D reconstruction, providing only a partial model of the scene. In contrast, this paper introduces the first Markov Random Field (MRF) optimisation framework for full 3D HS. First, an initial reconstruction is obtained by performing 2.5D MRF optimisation with visibility constraints from multiple viewpoints and fusing the different outp uts. Then, a refined 3D model is obtained through volumetric MRF optimisation using a tailored Iterative Conditional Modes (ICM) algorithm. The proposed approach is evaluated with both synthetic and real data. Results show that the proposed full 3D optimisation significantly increases both geometric and normal accuracy, being able to achieve sub-millimetre precision. Furthermore, the approach is shown to be robust to occlusions and noise. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.237.27.159

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Addari, G. and Guillemaut, J. (2019). An MRF Optimisation Framework for Full 3D Helmholtz Stereopsis. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4; ISSN 2184-4321, pages 725-736. DOI: 10.5220/0007407307250736

@conference{visapp19,
author={Gianmarco Addari. and Jean{-}Yves Guillemaut.},
title={An MRF Optimisation Framework for Full 3D Helmholtz Stereopsis},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={725-736},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007407307250736},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - An MRF Optimisation Framework for Full 3D Helmholtz Stereopsis
SN - 978-989-758-354-4
IS - 2184-4321
AU - Addari, G.
AU - Guillemaut, J.
PY - 2019
SP - 725
EP - 736
DO - 10.5220/0007407307250736