Progressive shape-distribution-encoder for 3D shape retrieval

Jin Xie, Fan Zhu, Guoxian Dai, Yi Fang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a deep shape descriptor by learning the shape distributions at different diffusion time via a progressive deep shape-distribution-encoder. First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometrical structure of the shape. Then, we propose to learn discriminative shape features through a progressive shapedistribution-encoder. Specially, the progressive shape-distributionencoder aims at modeling the complex non-linear transform of the estimated shape distributions between consecutive diffusion time. Furthermore, in order to characterize the intrinsic structure of the shape more efficiently, we stack multiple proposed progressive shapedistribution-encoders to form a neural network structure. Finally, we concatenated all neurons in the hidden layers of the progressive shape-distribution-encoder network to form a discriminative shape descriptor for retrieval. The proposed method is evaluated on three benchmark 3D shape datasets and the experimental results demonstrate the superiority of our method to the existing approaches.

Original languageEnglish (US)
Title of host publicationMM 2015 - Proceedings of the 2015 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages1167-1170
Number of pages4
ISBN (Electronic)9781450334594
DOIs
StatePublished - Oct 13 2015
Event23rd ACM International Conference on Multimedia, MM 2015 - Brisbane, Australia
Duration: Oct 26 2015Oct 30 2015

Publication series

NameMM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Other

Other23rd ACM International Conference on Multimedia, MM 2015
CountryAustralia
CityBrisbane
Period10/26/1510/30/15

Keywords

  • 3D shape retrieval
  • Denoising Auto-Encoder
  • Heat Diffusion
  • Heat Kernel Signature

ASJC Scopus subject areas

  • Media Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Software

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