Evaluation of 3D feature descriptors for multi-modal data registration
Evaluation of 3D feature descriptors for multi-modal data registration
We propose a framework for 2D/3D multi-modal data registration and evaluate 3D feature descriptors for registration of 3D datasets from different sources. 3D datasets of outdoor environments can be acquired using a variety of active and passive sensor technologies. Registration of these datasets into a common coordinate frame is required for subsequent modelling and visualisation. 2D images are converted into 3D structure by stereo or multiview reconstruction techniques and registered to a unified 3D domain with other datasets in a 3D world. Multi-modal datasets have different density, noise, and types of errors in geometry. This paper provides a performance benchmark for existing 3D feature descriptors across multi-modal datasets. This analysis highlights the limitations of existing 3D feature detectors and descriptors which need to be addressed for robust multi-modal data registration. We analyse and discuss the performance of existing methods in registering various types of datasets then identify future directions required to achieve robust multi-modal data registration. © 2013 IEEE.
2D/3D registration, Different densities, Evaluation, Feature descriptors, Feature detector, Multi-modal data, Multi-view reconstruction, Outdoor environment, Benchmarking, Data processing, Feature extraction, Modal analysis, Three dimensional
119-126
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db
29 June 2013
Kim, H.
2c7c135c-f00b-4409-acb2-85b3a9e8225f
Hilton, Adrian
12782a55-4c4d-4dfb-a690-62505f6665db
Kim, H. and Hilton, Adrian
(2013)
Evaluation of 3D feature descriptors for multi-modal data registration.
International Conference on 3D Vision, , Seattle, United States.
29 Jun - 01 Jul 2013.
.
(doi:10.1109/3DV.2013.24).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We propose a framework for 2D/3D multi-modal data registration and evaluate 3D feature descriptors for registration of 3D datasets from different sources. 3D datasets of outdoor environments can be acquired using a variety of active and passive sensor technologies. Registration of these datasets into a common coordinate frame is required for subsequent modelling and visualisation. 2D images are converted into 3D structure by stereo or multiview reconstruction techniques and registered to a unified 3D domain with other datasets in a 3D world. Multi-modal datasets have different density, noise, and types of errors in geometry. This paper provides a performance benchmark for existing 3D feature descriptors across multi-modal datasets. This analysis highlights the limitations of existing 3D feature detectors and descriptors which need to be addressed for robust multi-modal data registration. We analyse and discuss the performance of existing methods in registering various types of datasets then identify future directions required to achieve robust multi-modal data registration. © 2013 IEEE.
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Published date: 29 June 2013
Venue - Dates:
International Conference on 3D Vision, , Seattle, United States, 2013-06-29 - 2013-07-01
Keywords:
2D/3D registration, Different densities, Evaluation, Feature descriptors, Feature detector, Multi-modal data, Multi-view reconstruction, Outdoor environment, Benchmarking, Data processing, Feature extraction, Modal analysis, Three dimensional
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Local EPrints ID: 440582
URI: http://eprints.soton.ac.uk/id/eprint/440582
PURE UUID: 20c19141-7f5a-4b34-823f-ef7351743d00
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Date deposited: 07 May 2020 16:38
Last modified: 17 Mar 2024 04:01
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Contributors
Author:
H. Kim
Author:
Adrian Hilton
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