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Performance evaluation of 3D local feature descriptors
Journal article   Open access   Peer reviewed

Performance evaluation of 3D local feature descriptors

Y. Guo, M. Bennamoun, F. Sohel, M. Lu, J. Wan and J. Zhang
Computer Vision -- ACCV 2014, Vol.9004, pp.178-194
2015
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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.

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