Name
Abstract | ||
|---|---|---|
•A novel method which exploits similarities at the spectral frequencies of meshes.•Fast execution times even for dense models.•Preservation of features exploiting the low-rank spectral properties of the GFT.•Use of fixed parameters independently the 3D model or the type of noise. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.cag.2019.05.017 | Computers & Graphics |
Keywords | Field | DocType |
Spectral denoising via RPCA,Dynamic noisy 3D meshes,Laplacian matrix decomposition,Denoising of graph fourier transform | Noise reduction,Computer vision,Polygon mesh,Structured light,Computer science,Segmentation,Outlier,Robust principal component analysis,Artificial intelligence,Computer animation,Cognitive neuroscience of visual object recognition | Journal |
Volume | ISSN | Citations |
82 | 0097-8493 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gerasimos Arvanitis | 1 | 9 | 6.21 |
| Aris S. Lalos | 2 | 192 | 32.84 |
| K. Moustakas | 3 | 285 | 58.02 |