Performance Evaluation of 3D Local Feature Descriptors

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• 作者：Yulan Guo (17) (18)
  Mohammed Bennamoun (18)
  Ferdous Sohel (18)
  Min Lu (17)
  Jianwei Wan (17)
  Jun Zhang (17)
  
  17. College of Electronic Science and Engineering ; National University of Defense Technology ; Changsha ; China
  18. School of Computer Science and Software Engineering ; The University of Western Australia ; Crawley ; Australia
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：9004
• 期：1
• 页码：178-194
• 全文大小：1,289 KB
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• 作者单位：Computer Vision -- ACCV 2014
• 丛书名：978-3-319-16807-4
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.