Name
Abstract | ||
|---|---|---|
One of the challenging problems for shape editing is to adapt shapes with diversified structures for various editing needs. In this paper we introduce a shape editing approach that automatically adapts the structure of a shape being edited with respect to user inputs. Given a category of shapes, our approach first classifies them into groups based on the constituent parts. The group-sensitive priors, including both inter-group and intra-group priors, are then learned through statistical structure analysis and multivariate regression. By using these priors, the inherent characteristics and typical variations of shape structures can be well captured. Based on such group-sensitive priors, we propose a framework for real-time shape editing, which adapts the structure of shape to continuous user editing operations. Experimental results show that the proposed approach is capable of both structure-preserving and structure-varying shape editing. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1111/cgf.12808 | COMPUTER GRAPHICS FORUM |
Field | DocType | Volume |
Structure analysis,Active shape model,Computer vision,Pattern recognition,Computer science,Multivariate statistics,Artificial intelligence,Prior probability,Shape analysis (digital geometry) | Journal | 35.0 |
Issue | ISSN | Citations |
2.0 | 0167-7055 | 5 |
PageRank | References | Authors |
0.41 | 28 | 5 |