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General dynamic scene reconstruction from multiple view video

General dynamic scene reconstruction from multiple view video
General dynamic scene reconstruction from multiple view video
This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques or dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure, and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance.
900-908
Mustafa, Armin
29037014-ab45-4368-81e3-6b698e9bbbd0
Kim, Hansung
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Guillemaut, Jean-Yves
fcaefbd8-8ba1-4c43-978c-0b5d432a3285
Hilton, Adrian
e0bcaff3-221a-471b-a940-5b6783d21ff2
Mustafa, Armin
29037014-ab45-4368-81e3-6b698e9bbbd0
Kim, Hansung
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Guillemaut, Jean-Yves
fcaefbd8-8ba1-4c43-978c-0b5d432a3285
Hilton, Adrian
e0bcaff3-221a-471b-a940-5b6783d21ff2

Mustafa, Armin, Kim, Hansung, Guillemaut, Jean-Yves and Hilton, Adrian (2015) General dynamic scene reconstruction from multiple view video. 2015 IEEE International Conference on Computer Vision, Chile. 07 - 13 Dec 2015. pp. 900-908 . (doi:10.1109/ICCV.2015.109).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper introduces a general approach to dynamic scene reconstruction from multiple moving cameras without prior knowledge or limiting constraints on the scene structure, appearance, or illumination. Existing techniques or dynamic scene reconstruction from multiple wide-baseline camera views primarily focus on accurate reconstruction in controlled environments, where the cameras are fixed and calibrated and background is known. These approaches are not robust for general dynamic scenes captured with sparse moving cameras. Previous approaches for outdoor dynamic scene reconstruction assume prior knowledge of the static background appearance and structure. The primary contributions of this paper are twofold: an automatic method for initial coarse dynamic scene segmentation and reconstruction without prior knowledge of background appearance or structure, and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes from multiple wide-baseline static or moving cameras. Evaluation is performed on a variety of indoor and outdoor scenes with cluttered backgrounds and multiple dynamic non-rigid objects such as people. Comparison with state-of-the-art approaches demonstrates improved accuracy in both multiple view segmentation and dense reconstruction. The proposed approach also eliminates the requirement for prior knowledge of scene structure and appearance.

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More information

Published date: 7 December 2015
Venue - Dates: 2015 IEEE International Conference on Computer Vision, Chile, 2015-12-07 - 2015-12-13

Identifiers

Local EPrints ID: 438811
URI: http://eprints.soton.ac.uk/id/eprint/438811
PURE UUID: 7fef7cbc-08b4-4a5a-815e-143d33a5a517
ORCID for Hansung Kim: ORCID iD orcid.org/0000-0003-4907-0491

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Date deposited: 24 Mar 2020 17:52
Last modified: 20 May 2020 01:04

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Contributors

Author: Armin Mustafa
Author: Hansung Kim ORCID iD
Author: Jean-Yves Guillemaut
Author: Adrian Hilton

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