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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 1-3/2013

01.03.2013

A Performance Evaluation of Volumetric 3D Interest Point Detectors

verfasst von: Tsz-Ho Yu, Oliver J. Woodford, Roberto Cipolla

Erschienen in: International Journal of Computer Vision | Ausgabe 1-3/2013

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Abstract

This paper presents the first performance evaluation of interest points on scalar volumetric data. Such data encodes 3D shape, a fundamental property of objects. The use of another such property, texture (i.e. 2D surface colouration), or appearance, for object detection, recognition and registration has been well studied; 3D shape less so. However, the increasing prevalence of 3D shape acquisition techniques and the diminishing returns to be had from appearance alone have seen a surge in 3D shape-based methods. In this work, we investigate the performance of several state of the art interest points detectors in volumetric data, in terms of repeatability, number and nature of interest points. Such methods form the first step in many shape-based applications. Our detailed comparison, with both quantitative and qualitative measures on synthetic and real 3D data, both point-based and volumetric, aids readers in selecting a method suitable for their application.
Vorheriger Artikel A Machine-Learning Approach to Keypoint Detection and Landmarking on 3D Meshes
Nächster Artikel Performance Evaluation of 3D Keypoint Detectors

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Fußnoten
1
Covariant characteristics, often (inaccurately) referred to as invariant characteristics, undergo the same transformation as the data. We prefer “covariant” in order to distinguish truly invariant characteristics.
 
2
When referring to interest points in the context of methodology, we include image features such as corners, lines, edges and blobs.
 
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Metadaten
Titel
A Performance Evaluation of Volumetric 3D Interest Point Detectors
verfasst von
Tsz-Ho Yu
Oliver J. Woodford
Roberto Cipolla
Publikationsdatum
01.03.2013
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 1-3/2013
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-012-0563-2

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