research-article

Retrieval of non-rigid 3D shapes from multiple aspects

Authors:

Zhenzhong Kuang,

Hua LiAuthors Info & Claims

Computer-Aided Design, Volume 58, Issue C

Pages 13 - 23

https://doi.org/10.1016/j.cad.2014.08.004

Published: 01 January 2015 Publication History

Abstract

As non-rigid 3D shape plays increasingly important roles in practical applications, this paper addresses its retrieval problem by considering three aspects: shape representation, retrieval optimization, and shape filtering. (1) For shape representation, two kinds of features are considered. We first propose a new integration kernel based local descriptor, and then an efficient voting scheme is designed for shape representation. Besides, we also study the commute times as shape distributions, which grasp the spatial shape information globally. Both of them capture shape information from different viewpoints based on the same embedding basis. (2) We then study the typical problem of retrieval optimization. Prior works show poor stability under different similarity windows. To deal with this deficiency, we propose to model the problem as a distance mapping on a graph in spectral manifold space. (3) Usually, for each retrieval input, a list is returned and there may be lots of irrelevant results. We develop an algorithm to filter them out by combining multiple kernels. Finally, three public datasets are employed for performance evaluation and the results show that the studied techniques have contributed a lot in promoting the recognition rate of non-rigid 3D shapes. Multiple aspects are considered for non-rigid 3D shape retrieval.Two distinctive viewpoints are considered for shape representation.A new retrieval optimization approach is proposed.A shape filtering algorithm is designed to remove the junk shapes.

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Cited By

Kuang ZYu JZhu SLi ZFan J(2019)Effective 3-D Shape Retrieval by Integrating Traditional Descriptors and Pointwise ConvolutionIEEE Transactions on Multimedia10.1109/TMM.2019.291872921:12(3164-3177)Online publication date: 19-Nov-2019
https://dl.acm.org/doi/10.1109/TMM.2019.2918729
Mirloo MEbrahimnezhad H(2018)Non-rigid 3D object retrieval using directional graph representation of wave kernel signatureMultimedia Tools and Applications10.1007/s11042-017-4617-x77:6(6987-7011)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11042-017-4617-x
Kuang ZYu JZhu SLi ZFan J(2018)Deformable Point Cloud Recognition Using Intrinsic Function and Deep LearningAdvances in Multimedia Information Processing – PCM 201810.1007/978-3-030-00767-6_9(89-101)Online publication date: 21-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-00767-6_9
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Retrieval of non-rigid 3D shapes from multiple aspects
1. Computing methodologies
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Published In

cover image Computer-Aided Design

Computer-Aided Design Volume 58, Issue C

January 2015

258 pages

ISSN:0010-4485

Issue’s Table of Contents

Copyright © Elsevier Ltd.

Publisher

Butterworth-Heinemann

United States

Publication History

Published: 01 January 2015

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Cited By

Kuang ZYu JZhu SLi ZFan J(2019)Effective 3-D Shape Retrieval by Integrating Traditional Descriptors and Pointwise ConvolutionIEEE Transactions on Multimedia10.1109/TMM.2019.291872921:12(3164-3177)Online publication date: 19-Nov-2019
https://dl.acm.org/doi/10.1109/TMM.2019.2918729
Mirloo MEbrahimnezhad H(2018)Non-rigid 3D object retrieval using directional graph representation of wave kernel signatureMultimedia Tools and Applications10.1007/s11042-017-4617-x77:6(6987-7011)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1007/s11042-017-4617-x
Kuang ZYu JZhu SLi ZFan J(2018)Deformable Point Cloud Recognition Using Intrinsic Function and Deep LearningAdvances in Multimedia Information Processing – PCM 201810.1007/978-3-030-00767-6_9(89-101)Online publication date: 21-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-00767-6_9
Li PMa HMing A(2017)A non-rigid 3D model retrieval method based on scale-invariant heat kernel signature featuresMultimedia Tools and Applications10.1007/s11042-016-3606-976:7(10207-10230)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1007/s11042-016-3606-9
Sirin YDemirci M(2017)2D and 3D shape retrieval using skeleton filling rateMultimedia Tools and Applications10.1007/s11042-016-3422-276:6(7823-7848)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s11042-016-3422-2
Xin SWang WChen SZhao JShu Z(2016)Intrinsic Girth Function for Shape ProcessingACM Transactions on Graphics10.1145/286657035:3(1-14)Online publication date: 3-Apr-2016
https://dl.acm.org/doi/10.1145/2866570
Kuang ZLi ZLiu YZou C(2016)Shape similarity assessment based on partial feature aggregation and ranking listsPattern Recognition Letters10.1016/j.patrec.2016.05.02683:P3(368-378)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.patrec.2016.05.026
Shanmugavadivu PSumathy PVadivel A(2016)FOSIRNeurocomputing10.1016/j.neucom.2015.07.015171:C(719-735)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.07.015
Li ZKuang ZLiu YWang J(2016)Multiscale shape context and re-ranking for deformable shape retrievalComputers and Graphics10.1016/j.cag.2015.07.00254:C(8-17)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1016/j.cag.2015.07.002
Kuang ZLi ZLv QWeiwei TLiu Y(2015)Modal function transformation for isometric 3D shape representationComputers and Graphics10.1016/j.cag.2014.09.03346:C(209-220)Online publication date: 1-Feb-2015
https://dl.acm.org/doi/10.1016/j.cag.2014.09.033

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