Name
Abstract | ||
|---|---|---|
Considering the fact that points of interest on 3D shapes can be discriminated from a geometric perspective, it is reasonable to map the geometric signature of a point p to a probability value encoding to what degree p is a point of interest, especially for a specific class of 3D shapes. Based on the observation, we propose a three-phase algorithm for learning and predicting points of interest on ... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TVCG.2018.2848628 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | Field | DocType |
Shape,Three-dimensional displays,Neural networks,Feature extraction,Prediction algorithms,Solid modeling,Task analysis | Computer vision,Autoencoder,Pattern recognition,Computer science,Feature extraction,Probability distribution,Artificial intelligence,Solid modeling,Point of interest,Artificial neural network,Cluster analysis,Encoding (memory) | Journal |
Volume | Issue | ISSN |
25 | 8 | 1077-2626 |
Citations | PageRank | References |
3 | 0.41 | 38 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Shu | 1 | 42 | 5.05 |
| Shi-Qing Xin | 2 | 97 | 13.42 |
| Xin Xu | 3 | 162 | 40.08 |
| Ligang Liu | 4 | 1960 | 108.77 |
| Ladislav Kavan | 5 | 688 | 35.24 |