Name
Abstract | ||
|---|---|---|
Efficient parameterization of point-sampled surfaces is a fundamental problem in the field of digital geometry processing. In order to parameterize a given point-sampled surface for minimal distance distortion, a differentials-based segmentation and parameterization approach is proposed in this paper. Our approach partitions the point-sampled geometry based on two criteria: variation of Euclidean distance between sample points, and angular difference between surface differential directions. According to the analysis of normal curvatures for some specified directions, a new projection approach is adopted to estimate the local surface differentials. Then a k-means clustering (k-MC) algorithm is used for partitioning the model into a set of charts based on the estimated local surface attributes. Finally, each chart is parameterized with a statistical method -- multidimensional scaling (MDS) approach, and the parameterization results of all charts form an atlas for compact storage. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s11390-007-9088-5 | J. Comput. Sci. Technol. |
Keywords | Field | DocType |
new projection approach,segmentation,multidimensional scaling,parameterization result,parameterization,parameterization approach,euclidean distance,k-means clustering,efficient parameterization,estimated local surface attribute,local surface differential,point-sampled geometry,surface differential direction,differentials-based segmentation,computer graphics,point-sampled surface,k means clustering,digital geometry | k-means clustering,Mathematical optimization,Multidimensional scaling,Parametrization,Computer science,Segmentation,Euclidean distance,Algorithm,Real-time computing,Cluster analysis,Distortion,Digital geometry | Journal |
Volume | Issue | ISSN |
22 | 5 | 1860-4749 |
Citations | PageRank | References |
6 | 0.41 | 30 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongwei Miao | 1 | 77 | 7.51 |
| jieqing feng | 2 | 309 | 31.72 |
| Chunxia Xiao | 3 | 466 | 39.83 |
| Qun-Sheng Peng | 4 | 88 | 7.57 |
| A. R. Forrest | 5 | 6 | 0.41 |