Name
Abstract | ||
|---|---|---|
Point set filtering, which aims at reconstructing noise-free point sets from their corresponding noisy inputs, is a fundamental problem in 3D geometry processing. The main challenge of point set filtering is to preserve geometric features of the underlying geometry while at the same time removing the noise. State-of-the-art point set filtering methods still struggle with this issue: some are not d... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TVCG.2017.2725948 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | Field | DocType |
Surface reconstruction,Robustness,Noise measurement,Gaussian mixture model,Geometry,Three-dimensional displays | Computer vision,Pattern recognition,3d geometry,Noise measurement,Computer science,Filter (signal processing),Moving least squares,Robustness (computer science),Artificial intelligence,Point set,Mixture model,Machine learning | Journal |
Volume | Issue | ISSN |
24 | 8 | 1077-2626 |
Citations | PageRank | References |
6 | 0.39 | 31 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuequan Lu | 1 | 64 | 17.63 |
| Shihao Wu | 2 | 165 | 7.84 |
| Honghua Chen | 3 | 6 | 2.76 |
| Sai Kit Yeung | 4 | 60 | 4.97 |
| Wenzhi Chen | 5 | 141 | 28.65 |
| Zwicker Matthias | 6 | 2513 | 129.25 |