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An evaluation of canonical forms for non-rigid 3D shape retrieval
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10.1016:j.gmod.2018.02.002.pdf
Date
2018-05-01
Author
Pickup, David
Liu, Juncheng
Sun, Xianfang
Rosin, Paul L.
Martin, Ralph R.
Cheng, Zhiquan
Lian, Zhouhui
Nie, Sipin
Jin, Longcun
Shamai, Gil
Sahillioğlu, Yusuf
Kavan, Ladislav
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
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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.
Subject Keywords
Modelling and Simulation
,
Software
,
Geometry and Topology
,
Computer Graphics and Computer-Aided Design
,
Geometry processing
,
Canonical forms
,
3D Shape retrieval
URI
https://hdl.handle.net/11511/38372
Journal
GRAPHICAL MODELS
DOI
https://doi.org/10.1016/j.gmod.2018.02.002
Collections
Department of Computer Engineering, Article