The University of Southampton
University of Southampton Institutional Repository

Evaluation of 3D feature descriptors for multi-modal data registration

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
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, United States. 29 Jun - 01 Jul 2013. pp. 119-126 . (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.

Full text not available from this repository.

More information

Published date: 29 June 2013
Venue - Dates: International Conference on 3D Vision, 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

Identifiers

Local EPrints ID: 440582
URI: http://eprints.soton.ac.uk/id/eprint/440582
PURE UUID: 20c19141-7f5a-4b34-823f-ef7351743d00
ORCID for H. Kim: ORCID iD orcid.org/0000-0003-4907-0491

Catalogue record

Date deposited: 07 May 2020 16:38
Last modified: 23 May 2020 00:47

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×