Name
Title | ||
|---|---|---|
ESimp: Error-Controllable Simplification with Feature Preservation for Surface Reconstruction |
Abstract | ||
|---|---|---|
We present a rapid and effective point simplification algorithm for surface reconstruction which can represent different levels-of-detail. The core of this algorithm is to generate an approximately minimal set of adaptive balls covering the whole surface by defining and minimizing local quadric error functions. First, the feature points are extracted by simple thresholding curvatures, Second, for the non-feature points, they are covered by distinct balls. The size of each ball varies and reflects how curved the local surface is. Once the size of radius is fixed, the points in each ball will be substituted by an optimized point. Thus, the simplified surface consists of extracted feature points and optimized points. we can employ this algorithm to produce coarse-to-fine models by controlling a general error level, and name it as ESimp for short. Worthy of note, the error level of each ball may be adaptively adjusted according to the local curvature and density of the center of this ball which can avoid holes generation. Finally, the simplified points are triangulated by Cocone algorithm. This algorithm has been applied to a set of large scanned models. Experimental results demonstrate that it can generate high-quality surface approximation with feature preservation. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CW.2011.46 | CW |
Keywords | Field | DocType |
distinct ball,adaptive ball,high-quality surface approximation,whole surface,feature point,cocone algorithm,optimized point,surface reconstruction,feature preservation,local surface,error-controllable simplification,effective point simplification algorithm,approximation algorithms,feature extraction,computer graphics,mathematical model,clustering algorithms,algorithm design and analysis | Surface reconstruction,Approximation algorithm,Computer vision,Algorithm design,Curvature,Computer science,Ball (bearing),Feature extraction,Artificial intelligence,Thresholding,Quadric | Conference |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingqiang Wei | 1 | 125 | 22.66 |
| Yichen Li | 2 | 2 | 5.79 |
| Jianhuang Wu | 3 | 60 | 11.75 |
| Ming-Yong Pang | 4 | 22 | 11.44 |