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.

@inproceedings{facil2019camconvs,
    author = {Facil, Jose M. and Ummenhofer, Benjamin and Zhou, Huizhong and
    Montesano, Luis and Brox, Thomas and Civera, Javier},
    title = {{CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth}},
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2019},
    url = "https://webdiis.unizar.es/~jmfacil/camconvs/"
}