Name
Abstract | ||
|---|---|---|
We present a Multi View Stereo approach for huge unstructured image datasets that can deal with large variations in surface sampling rate of single images. Our method reconstructs surface parts always in the best available resolution. It considers scaling not only for large scale differences, but also between arbitrary small ones for a weighted merging of the best partial reconstructions. We create depth maps with our GPU based depth map algorithm, that also performs normal optimization. It matches several images that are found with a heuristic image selection method, to a reference image. We remove outliers by comparing depth maps against each other with a fast but reliable GPU approach. Then, we merge the different reconstructions from depth maps in 3D space by selecting the best points and optimizing them with not selected points. Finally, we create the surface by using a Delaunay graph cut. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33712-3_29 | ECCV (3) |
Keywords | Field | DocType |
depth map algorithm,robust multi view stereo,huge unstructured image datasets,best point,reference image,depth map,multi view stereo approach,large variation,single image,heuristic image selection method,large scale difference | Computer vision,Heuristic,Visual hull,Computer science,Reference image,Sampling (signal processing),Outlier,Artificial intelligence,Depth map,Point cloud,Scaling,Machine learning | Conference |
Volume | ISSN | Citations |
7574 | 0302-9743 | 26 |
PageRank | References | Authors |
0.87 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christian Bailer | 1 | 26 | 0.87 |
| Manuel Finckh | 2 | 47 | 2.34 |
| Hendrik P. A. Lensch | 3 | 1471 | 96.59 |