Name
Abstract | ||
|---|---|---|
Scene understanding requires reasoning about both what we can see and what is occluded. We offer a simple and general approach to infer labels of occluded background regions. Our approach incorporates estimates of visible surrounding background, detected objects, and shape priors from transferred training regions. We demonstrate the ability to infer the labels of occluded background regions in both the outdoor StreetScenes dataset and an indoor scene dataset using the same approach. Our experiments show that our method outperforms competent baselines. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33715-4_55 | ECCV (5) |
Keywords | Field | DocType |
scene understanding,indoor scene dataset,visible surrounding background,shape prior,competent baselines,general approach,occluded background region,underlying surface,outdoor streetscenes dataset,training region | Computer vision,Computer science,Artificial intelligence,Line-of-sight,Prior probability | Conference |
Volume | ISSN | Citations |
7576 | 0302-9743 | 26 |
PageRank | References | Authors |
1.09 | 20 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruiqi Guo | 1 | 564 | 22.10 |
| Derek Hoiem | 2 | 4998 | 302.66 |