  Monocular depth estimation from RGB images plays a pivotal role in 3D vision.
However, its accuracy can deteriorate in challenging environments such as
nighttime or adverse weather conditions. While long-wave infrared cameras offer
stable imaging in such challenging conditions, they are inherently
low-resolution, lacking rich texture and semantics as delivered by the RGB
image. Current methods focus solely on a single modality due to the
difficulties to identify and integrate faithful depth cues from both sources.
To address these issues, this paper presents a novel approach that identifies
and integrates dominant cross-modality depth features with a learning-based
framework. Concretely, we independently compute the coarse depth maps with
separate networks by fully utilizing the individual depth cues from each
modality. As the advantageous depth spreads across both modalities, we propose
a novel confidence loss steering a confidence predictor network to yield a
confidence map specifying latent potential depth areas. With the resulting
confidence map, we propose a multi-modal fusion network that fuses the final
depth in an end-to-end manner. Harnessing the proposed pipeline, our method
demonstrates the ability of robust depth estimation in a variety of difficult
scenarios. Experimental results on the challenging MS$^2$ and ViViD++ datasets
demonstrate the effectiveness and robustness of our method.
