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3D moving object reconstruction by temporal accumulation

3D moving object reconstruction by temporal accumulation
3D moving object reconstruction by temporal accumulation
Much progress has been made recently in the development of 3D acquisition technologies, which increased the availability of low-cost 3D sensors, such as the Microsoft Kinect. This promotes a wide variety of computer vision applications needing object recognition and 3D reconstruction. We present a novel algorithm for full 3D reconstruction of unknown rotating objects in 2.5D point cloud sequences, such as those generated by 3D sensors. Our algorithm incorporates structural and temporal motion information to build 3D models of moving objects and is based on motion compensated temporal accumulation. The proposed algorithm requires only the fixed centre or axis of rotation, unlike other 3D reconstruction methods, it does not require key point detection, feature description, correspondence matching, provided object models or any geometric information about the object. Moreover, our algorithm integrally estimates the best rigid transformation parameters for registration, applies surface resembling, reduces noise and estimates the optimum angular velocity of the rotating object.
point clouds, 3D reconstruction, kinect
2125-2130
Abuzaina, Anas
8e37a6fc-d659-45a0-8b8b-e885f385ece5
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Abuzaina, Anas
8e37a6fc-d659-45a0-8b8b-e885f385ece5
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da

Abuzaina, Anas, Nixon, Mark S. and Carter, John N. (2014) 3D moving object reconstruction by temporal accumulation. 2014 22nd International Conference on Pattern Recognition (ICPR), Sweden. pp. 2125-2130 . (doi:10.1109/ICPR.2014.370).

Record type: Conference or Workshop Item (Paper)

Abstract

Much progress has been made recently in the development of 3D acquisition technologies, which increased the availability of low-cost 3D sensors, such as the Microsoft Kinect. This promotes a wide variety of computer vision applications needing object recognition and 3D reconstruction. We present a novel algorithm for full 3D reconstruction of unknown rotating objects in 2.5D point cloud sequences, such as those generated by 3D sensors. Our algorithm incorporates structural and temporal motion information to build 3D models of moving objects and is based on motion compensated temporal accumulation. The proposed algorithm requires only the fixed centre or axis of rotation, unlike other 3D reconstruction methods, it does not require key point detection, feature description, correspondence matching, provided object models or any geometric information about the object. Moreover, our algorithm integrally estimates the best rigid transformation parameters for registration, applies surface resembling, reduces noise and estimates the optimum angular velocity of the rotating object.

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More information

Published date: 25 August 2014
Venue - Dates: 2014 22nd International Conference on Pattern Recognition (ICPR), Sweden, 2014-08-25
Related URLs:
Keywords: point clouds, 3D reconstruction, kinect
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 374261
URI: http://eprints.soton.ac.uk/id/eprint/374261
PURE UUID: e7e43067-edc4-438e-8201-3120d5aa000e
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 11 Feb 2015 12:11
Last modified: 20 Jul 2019 01:28

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