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

01.03.2013

Performance Evaluation of 3D Keypoint Detectors

verfasst von: Federico Tombari, Samuele Salti, Luigi Di Stefano

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

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Abstract

In the past few years detection of repeatable and distinctive keypoints on 3D surfaces has been the focus of intense research activity, due on the one hand to the increasing diffusion of low-cost 3D sensors, on the other to the growing importance of applications such as 3D shape retrieval and 3D object recognition. This work aims at contributing to the maturity of this field by a thorough evaluation of several recent 3D keypoint detectors. A categorization of existing methods in two classes, that allows for highlighting their common traits, is proposed, so as to abstract all algorithms to two general structures. Moreover, a comprehensive experimental evaluation is carried out in terms of repeatability, distinctiveness and computational efficiency, based on a vast data corpus characterized by nuisances such as noise, clutter, occlusions and viewpoint changes.
Vorheriger Artikel A Performance Evaluation of Volumetric 3D Interest Point Detectors
Nächster Artikel Feature-Preserved 3D Canonical Form

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Fußnoten
8
The requirement of 16 GB of RAM for 100 K vertices was confirmed by personal communication with the authors.
 
9
Recently, the detector proposed in Novatnack and Nishino (2008) has been used (Bariya and Nishino 2010) for object recognition on the Laser Scanner dataset by synthesizing range images from a number of uniformly distributed overlapping views of the 3D model of the object. This technique is not suitable for our experimental comparison because the performance of detectors working on range images will be influenced by external factors such as synthetic views position and distribution.
 
10
As MeshDoG implements a non-linear scale increment within its scale-space, we tuned its scale-space parameters so as to best approximate the same scale set used by the other methods.
 
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Metadaten
Titel
Performance Evaluation of 3D Keypoint Detectors
verfasst von
Federico Tombari
Samuele Salti
Luigi Di Stefano
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/s11263-012-0545-4

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