  With the development of computational intelligence algorithms, unsupervised
monocular depth and pose estimation framework, which is driven by warped
photometric consistency, has shown great performance in the daytime scenario.
While in some challenging environments, like night and rainy night, the
essential photometric consistency hypothesis is untenable because of the
complex lighting and reflection, so that the above unsupervised framework
cannot be directly applied to these complex scenarios. In this paper, we
investigate the problem of unsupervised monocular depth estimation in highly
complex scenarios and address this challenging problem by adopting an image
transfer-based domain adaptation framework. We adapt the depth model trained on
day-time scenarios to be applicable to night-time scenarios, and constraints on
both feature space and output space promote the framework to learn the key
features for depth decoding. Meanwhile, we further tackle the effects of
unstable image transfer quality on domain adaptation, and an image adaptation
approach is proposed to evaluate the quality of transferred images and
re-weight the corresponding losses, so as to improve the performance of the
adapted depth model. Extensive experiments show the effectiveness of the
proposed unsupervised framework in estimating the dense depth map from highly
complex images.
