CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth

Jose M. Facil, Benjamin Ummenhofer, Huizhong Zhou, Luis Montesano, Thomas Brox, Javier Civera; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11826-11835

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.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Facil_2019_CVPR,
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 = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}