Multi-view pairwise relationship learning for sketch based 3D shape retrieval

Hanhui Li, Hefeng Wu, Xiangjian He, Shujin Lin, Ruomei Wang, Xiaonan Luo

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

Recent progress in sketch-based 3D shape retrieval creates a novel and user-friendly way to explore massive 3D shapes on the Internet. However, current methods on this topic rely on designing invariant features for both sketches and 3D shapes, or complex matching strategies. Therefore, they suffer from problems like arbitrary drawings and inconsistent viewpoints. To tackle this problem, we propose a probabilistic framework based on Multi-View Pairwise Relationship (MVPR) learning. Our framework includes multiple views of 3D shapes as the intermediate layer between sketches and 3D shapes, and transforms the original retrieval problem into the form of inferring pairwise relationship between sketches and views. We accomplish pairwise relationship inference by a novel MVPR net, which can automatically predict and merge the pairwise relationships between a sketch and multiple views, thus freeing us from exhaustively selecting the best view of 3D shapes. We also propose to learn robust features for sketches and views via fine-tuning pre-trained networks. Extensive experiments on a large dataset demonstrate that the proposed method can outperform state-of-the-art methods significantly.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Pages1434-1439
Number of pages6
ISBN (Electronic)9781509060672
DOIs
Publication statusPublished - 28 Aug 2017
Externally publishedYes
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: 10 Jul 201714 Jul 2017

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2017 IEEE International Conference on Multimedia and Expo, ICME 2017
Country/TerritoryHong Kong
CityHong Kong
Period10/07/1714/07/17

Keywords

  • 3D Shape Retrieval
  • Semantic Similarity
  • Sketch

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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