Style-sensitive 3D model retrieval through sketch-based queries

Bin Cao, Yang Kang, Shujin Lin, Xiaonan Luo, Songhua Xu, Zhihan Lv

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Traditional sketch-based 3D model retrieval methods are content-based, which return the search results by ranking the geometric similarities among a free-hand drawing and 3D model candidates. These conventional methods do not consider personal drawing characteristics and styles (abbreviated as styles), which are obvious and important in user's sketch queries. An ordinary user presumably is not a professional and skillful artist. Therefore, users are likely to introduce personal drawing style in sketching 3D model rather than faithfully render the model according to its geometric perspectives. For amateurs, such personal styles are unintentionally introduced due to their limited sketching capabilities. As determined by a person's sketching habit, personal drawing styles are largely personally consistent and stable. Ignoring such non-trivial personal styles while attempting to reconstruct intended models according to their sketch inputs does not usually produce satisfactory outcomes, in particular, for amateur sketchers. To overcome this problem, we propose a novel style-sensitive 3D model retrieval method based on three-view user sketch inputs. The new method models users' personal sketching styles and constructs joint tensor factorization to improve the retrieval performance.

Original languageEnglish (US)
Pages (from-to)2637-2644
Number of pages8
JournalJournal of Intelligent and Fuzzy Systems
Volume31
Issue number5
DOIs
StatePublished - Oct 13 2016

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Engineering(all)
  • Artificial Intelligence

Keywords

  • Three views
  • cluster
  • gabor filter
  • style
  • tensor factorization

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