  Cross-spectrum depth estimation aims to provide a depth map in all
illumination conditions with a pair of dual-spectrum images. It is valuable for
autonomous vehicle applications when the vehicle is equipped with two cameras
of different modalities. However, images captured by different-modality cameras
can be photometrically quite different. Therefore, cross-spectrum depth
estimation is a very challenging problem. Moreover, the shortage of large-scale
open-source datasets also retards further research in this field. In this
paper, we propose an unsupervised visible-light image guided cross-spectrum
(i.e., thermal and visible-light, TIR-VIS in short) depth estimation framework
given a pair of RGB and thermal images captured from a visible-light camera and
a thermal one. We first adopt a base depth estimation network using RGB-image
pairs. Then we propose a multi-scale feature transfer network to transfer
features from the TIR-VIS domain to the VIS domain at the feature level to fit
the trained depth estimation network. At last, we propose a cross-spectrum
depth cycle consistency to improve the depth result of dual-spectrum image
pairs. Meanwhile, we release a large dual-spectrum depth estimation dataset
with visible-light and far-infrared stereo images captured in different scenes
to the society. The experiment result shows that our method achieves better
performance than the compared existing methods. Our datasets is available at
https://github.com/whitecrow1027/VIS-TIR-Datasets.
