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Linearizing the Plenoptic Space

Authors :
James L. Crowley
Grégoire Nieto
Frédéric Devernay
Intuitive Modeling and Animation for Interactive Graphics & Narrative Environments (IMAGINE )
Inria Grenoble - Rhône-Alpes
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Laboratoire Jean Kuntzmann (LJK )
Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Interaction située avec les objets et environnements intelligents (PERVASIVE)
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Informatique de Grenoble (LIG )
Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])
Source :
Light Fields for Computer Vision, Light Fields for Computer Vision, Jul 2017, Honolulu, United States. pp.1714-1725, ⟨10.1109/CVPRW.2017.218⟩, CVPR Workshops
Publication Year :
2017
Publisher :
HAL CCSD, 2017.

Abstract

International audience; The plenoptic function, also known as the light field or the lumigraph, contains the information about the radiance of all optical rays that go through all points in space in a scene. Since no camera can capture all this information, one of the main challenges in plenoptic imaging is light field reconstruction, which consists in interpolating the ray samples captured by the cameras to create a dense light field. Most existing methods perform this task by first attempting some kind of 3D reconstruction of the visible scene. Our method, in contrast, works by modeling the scene as a set of visual points, which describe how each point moves in the image when a camera moves. We compute visual point models of various degrees of complexity, and show that high-dimensional models are able to replicate complex optical effects such as reflection or refraction, and a model selection method can differentiate quasi-Lambertian from non-Lambertian areas in the scene.

Details

Language :
English
Database :
OpenAIRE
Journal :
Light Fields for Computer Vision, Light Fields for Computer Vision, Jul 2017, Honolulu, United States. pp.1714-1725, ⟨10.1109/CVPRW.2017.218⟩, CVPR Workshops
Accession number :
edsair.doi.dedup.....81647d6c0d8e71f4338bc5f1f603df78
Full Text :
https://doi.org/10.1109/CVPRW.2017.218⟩