Name
Abstract | ||
|---|---|---|
Single-view depth estimation suffers from the problem that a network trained on images from one camera does not generalize to images taken with a different camera model. Thus, changing the camera model requires collecting an entirely new training dataset. In this work, we propose a new type of convolution that can take the camera parameters into account, thus allowing neural networks to learn calibration-aware patterns. Experiments confirm that this improves the generalization capabilities of depth prediction networks considerably, and clearly outperforms the state of the art when the train and test images are acquired with different cameras. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CVPR.2019.01210 | 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) |
Field | DocType | Volume |
Computer vision,Computer science,Convolution,Artificial intelligence | Journal | abs/1904.02028 |
ISSN | Citations | PageRank |
1063-6919 | 2 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José M. Fácil | 1 | 29 | 1.90 |
| Benjamin Ummenhofer | 2 | 52 | 3.35 |
| Huizhong Zhou | 3 | 2 | 0.36 |
| L. Montesano | 4 | 653 | 42.58 |
| Thomas Brox | 5 | 7866 | 327.52 |
| Javier Civera | 6 | 756 | 48.61 |