Journal article
A comprehensive performance evaluation of 3D local feature descriptors
International Journal of Computer Vision, Vol.116(1), pp.66-89
2015
Abstract
A number of 3D local feature descriptors have been proposed in the literature. It is however, unclear which descriptors are more appropriate for a particular application. A good descriptor should be descriptive, compact, and robust to a set of nuisances. This paper compares ten popular local feature descriptors in the contexts of 3D object recognition, 3D shape retrieval, and 3D modeling. We first evaluate the descriptiveness of these descriptors on eight popular datasets which were acquired using different techniques. We then analyze their compactness using the recall of feature matching per each float value in the descriptor. We also test the robustness of the selected descriptors with respect to support radius variations, Gaussian noise, shot noise, varying mesh resolution, distance to the mesh boundary, keypoint localization error, occlusion, clutter, and dataset size. Moreover, we present the performance results of these descriptors when combined with different 3D keypoint detection methods. We finally analyze the computational efficiency for generating each descriptor.
Details
- Title
- A comprehensive performance evaluation of 3D local feature descriptors
- Authors/Creators
- Y. Guo (Author/Creator) - National University of Defense TechnologyM. Bennamoun (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - The University of Western AustraliaM. Lu (Author/Creator) - National University of Defense TechnologyJ. Wan (Author/Creator) - National University of Defense TechnologyN.M. Kwok (Author/Creator) - UNSW Sydney
- Publication Details
- International Journal of Computer Vision, Vol.116(1), pp.66-89
- Publisher
- Springer US
- Identifiers
- 991005543227607891
- Copyright
- © 2015 Springer Science+Business Media New York
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
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- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.116 Robotics
- 4.116.133 Simultaneous Localization and Mapping
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Engineering