Name
Abstract | ||
|---|---|---|
We presented two integrated solution schemes, sectional feature based strategy and surface feature based strategy, for modeling industrial components from point cloud to surfaces without using triangulation. For the sectional feature based strategy, slicing, curve feature recognition and constrained fitting are introduced. This strategy emphasizes the advanced feature architecture patterns from 2D to 3D in reverse engineering. The surface feature based strategy relies on differential geometric attributes estimation and diverse feature extraction techniques. The methods and algorithms such as attributes estimation based on 4D Shepard surface, symmetry plane extraction, quadric surface recognition and optimization, extruded and rotational surface extraction, and blend feature extraction with probability and statistic theory are proposed. The reliable three-dimensional feature fabricated the valid substratum of B-rep model faultlessly. All the algorithms are implemented in RE-SOFT, a reverse engineering software developed by Zhejiang University. The proposed strategies can be used to capture the original design intention accurately and to complete the reverse modeling process conveniently. Typical industrial components are used to illustrate the validation of our feature-based strategies. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.cad.2005.12.002 | Computer-Aided Design |
Keywords | Field | DocType |
reliable three-dimensional feature,reverse engineering,feature recognition,diverse feature extraction technique,quadric surface recognition,geometric constraints,constrained fitting,advanced feature architecture pattern,sectional feature,feature-based strategy,feature-based reverse modeling strategy,curve feature recognition,proposed strategy,blend feature extraction,shepard surface,probability and statistics,three dimensional,architectural pattern,software development,feature extraction,point cloud | k-nearest neighbors algorithm,Data mining,Dimensionality reduction,Feature (computer vision),Feature recognition,Feature extraction,Feature model,Feature (machine learning),Kanade–Lucas–Tomasi feature tracker,Mathematics | Journal |
Volume | Issue | ISSN |
38 | 5 | Computer-Aided Design |
Citations | PageRank | References |
13 | 0.57 | 41 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yinling Ke | 1 | 13 | 0.57 |
| Shuqian Fan | 2 | 15 | 0.99 |
| W. Zhu | 3 | 36 | 6.17 |
| An Li | 4 | 13 | 0.57 |
| Fengshan Liu | 5 | 76 | 11.78 |
| Xiquan Shi | 6 | 93 | 12.31 |