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An evaluation of canonical forms for non-rigid 3D shape retrieval

Pickup, David, Liu, Juncheng, Sun, Xianfang ORCID:, Rosin, Paul L. ORCID:, Martin, Ralph R., Cheng, Zhiquan, Lian, Zhouhui, Nie, Sipin, Jin, Longcun, Shamai, Gil, Sahillioğlu, Yusuf and Kavan, Ladislav 2018. An evaluation of canonical forms for non-rigid 3D shape retrieval. Graphical Models 97 , pp. 17-29. 10.1016/j.gmod.2018.02.002

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Canonical forms attempt to factor out a non-rigid shape’s pose, giving a pose-neutral shape. This opens up the possibility of using methods originally designed for rigid shape retrieval for the task of non-rigid shape retrieval. We extend our recent benchmark for testing canonical form algorithms. Our new benchmark is used to evaluate a greater number of state-of-the-art canonical forms, on five recent non-rigid retrieval datasets, within two different retrieval frameworks. A total of fifteen different canonical form methods are compared. We find that the difference in retrieval accuracy between different canonical form methods is small, but varies significantly across different datasets. We also find that efficiency is the main difference between the methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: Open access Attribution 4.0 International (CC BY 4.0)
Publisher: Elsevier
ISSN: 1524-0703
Funders: EPSRC
Date of First Compliant Deposit: 8 March 2018
Date of Acceptance: 15 February 2018
Last Modified: 06 May 2023 05:19

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