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Authors: Bruno Augusto Dorta Marques 1 ; Rafael Rego Drumond 2 ; Cristina Nader Vasconcelos 1 and Esteban Clua 1

Affiliations: 1 Universidade Federal Fluminense, Brazil ; 2 Universität Hildesheim, Germany

ISBN: 978-989-758-287-5

Keyword(s): Mixed Reality, Deep Learning, Light Source Estimation.

Related Ontology Subjects/Areas/Topics: Augmented, Mixed and Virtual Environments ; Computer Vision, Visualization and Computer Graphics ; Interactive Environments ; Lighting and Appearance ; Rendering

Abstract: Mixed reality is the union of virtual and real elements in a single scene. In this composition, of real and virtual elements, perceptual discrepancies in the illumination of objects may occur. We call these discrepancies the illumination mismatch problem. Recovering the lighting information from a real scene is a difficult task. Usually, such task requires prior knowledge of the scene, such as the scene geometry and special measuring equipment. We present a deep learning based technique that estimates point light source position from a single color image. The estimated light source position is used to create a composite image containing both the real and virtual environments. The proposed technique allows the final composite image to have consistent illumination between the real and virtual worlds, effectively reducing the effects of the illumination mismatch in Mixed Reality applications.

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Paper citation in several formats:
Marques, B.; Drumond, R.; Vasconcelos, C. and Clua, E. (2018). Deep Light Source Estimation for Mixed Reality.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1 GRAPP: GRAPP, ISBN 978-989-758-287-5, pages 303-311. DOI: 10.5220/0006724303030311

@conference{grapp18,
author={Bruno Augusto Dorta Marques. and Rafael Rego Drumond. and Cristina Nader Vasconcelos. and Esteban Clua.},
title={Deep Light Source Estimation for Mixed Reality},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1 GRAPP: GRAPP,},
year={2018},
pages={303-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006724303030311},
isbn={978-989-758-287-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1 GRAPP: GRAPP,
TI - Deep Light Source Estimation for Mixed Reality
SN - 978-989-758-287-5
AU - Marques, B.
AU - Drumond, R.
AU - Vasconcelos, C.
AU - Clua, E.
PY - 2018
SP - 303
EP - 311
DO - 10.5220/0006724303030311

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