Name
Abstract | ||
|---|---|---|
This work presents algorithms to resample and filter point cloud data reconstructed from multiple cameras and multiple time instants. In an initial resampling stage, a voxel or a surface mesh based approach resamples the point cloud data into a common sampling grid. Subsequently, the resampled data undergoes a filtering stage based on clustering to remove artifacts and achieve spatiotemporal consistency across cameras and time instants. The presented algorithms are evaluated in a view synthesis scenario. Results show that view synthesis with enhanced depth maps as produced by the algorithms leads to less artifacts than synthesis with the original source data. |
Year | Venue | Keywords |
|---|---|---|
2014 | 2014 International Conference on Computer Vision Theory and Applications (VISAPP) | Point Cloud Filtering,Multiview Resampling,Spatiotemporal Consistency |
Field | DocType | Volume |
Computer vision,Pattern recognition,Source data,Computer science,Filter (signal processing),View synthesis,Artificial intelligence,Sampling (statistics),Cluster analysis,Point cloud,Resampling,Grid | Conference | 3 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Skupin | 1 | 5 | 1.52 |
| Thilo Borgmann | 2 | 0 | 0.34 |
| Thomas Sikora | 3 | 257 | 22.57 |