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Hybrid 3D feature description and matching for multi-modal data registration

Hybrid 3D feature description and matching for multi-modal data registration
Hybrid 3D feature description and matching for multi-modal data registration
We propose a robust 3D feature description and registration method for 3D models reconstructed from various sensor devices. General 3D feature detectors and descriptors generally show low distinctiveness and repeatability for matching between different data modalities due to differences in noise and errors in geometry. The proposed method considers not only local 3D points but also neighbouring 3D keypoints to improve keypoint matching. The proposed method is tested on various multi-modal datasets including LIDAR scans, multiple photos, spherical images and RGBD videos to evaluate the performance against existing methods. © 2014 IEEE.
Feature extraction, Image processing, Modal analysis, 2D/3D registration, Feature description, Feature descriptors, Feature detector, Key point matching, Multi-modal data, Registration methods, Spherical images, Three dimensional computer graphics
3493-3497
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 (2014) Hybrid 3D feature description and matching for multi-modal data registration. IEEE International Conference on Image Processing, France. 27 - 30 Oct 2014. pp. 3493-3497 . (doi:10.1109/ICIP.2014.7025709).

Record type: Conference or Workshop Item (Paper)

Abstract

We propose a robust 3D feature description and registration method for 3D models reconstructed from various sensor devices. General 3D feature detectors and descriptors generally show low distinctiveness and repeatability for matching between different data modalities due to differences in noise and errors in geometry. The proposed method considers not only local 3D points but also neighbouring 3D keypoints to improve keypoint matching. The proposed method is tested on various multi-modal datasets including LIDAR scans, multiple photos, spherical images and RGBD videos to evaluate the performance against existing methods. © 2014 IEEE.

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More information

Published date: 27 October 2014
Venue - Dates: IEEE International Conference on Image Processing, France, 2014-10-27 - 2014-10-30
Keywords: Feature extraction, Image processing, Modal analysis, 2D/3D registration, Feature description, Feature descriptors, Feature detector, Key point matching, Multi-modal data, Registration methods, Spherical images, Three dimensional computer graphics

Identifiers

Local EPrints ID: 440584
URI: http://eprints.soton.ac.uk/id/eprint/440584
PURE UUID: 955d3997-81b1-47aa-81c7-742177073d07
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

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