Affiliations: [a]
School of Computer Science and Engineering, Hebei University of Technology, Tianjin, China
| [b]
National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou, China
| [c] Hebei Province Key Laboratory of Big Data Calculation, China
| [d]
School of Communication and Design, Sun Yat-sen University, Guangzhou, China
| [e]
Information Systems Department, New Jersey Institute of Technology, Newark, NJ, USA
| [f] SIAT, Chinese Academy of Science, Shenzhen, China
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.
Keywords: Three views, gabor filter, tensor factorization, style, cluster
