Journal article
Performance evaluation of 3D local feature descriptors
Computer Vision -- ACCV 2014, Vol.9004, pp.178-194
2015
Abstract
A number of 3D local feature descriptors have been proposed in literature. It is however, unclear which descriptors are more appropriate for a particular application. This paper compares nine popular local descriptors in the context of 3D shape retrieval, 3D object recognition, and 3D modeling. We first evaluate these descriptors on six popular datasets in terms of descriptiveness. We then test their robustness with respect to support radius, Gaussian noise, shot noise, varying mesh resolution, image boundary, and keypoint localization errors. Our extensive tests show that Tri-Spin-Images (TriSI) has the best overall performance across all datasets. Unique Shape Context (USC), Rotational Projection Statistics (RoPS), 3D Shape Context (3DSC), and Signature of Histograms of OrienTations (SHOT) also achieved overall acceptable results.
Details
- Title
- 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 TechnologyJ. Zhang (Author/Creator) - National University of Defense Technology
- Publication Details
- Computer Vision -- ACCV 2014, Vol.9004, pp.178-194
- Publisher
- Springer Verlag
- Identifiers
- 991005543310407891
- Copyright
- 2015 Springer International Publishing Switzerland
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
- Note
- Book Subtitle: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part II
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