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Performance evaluation of 3D local feature descriptors

Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J. and Zhang, J. (2015) Performance evaluation of 3D local feature descriptors. Lecture Notes in Computer Science, 9004 . pp. 178-194.

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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.

Publication Type: Journal Article
Publisher: Springer Verlag
Copyright: 2015 Springer International Publishing Switzerland
Notes: Book Subtitle: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part II
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