  Self-supervised monocular depth estimation methods have been increasingly
given much attention due to the benefit of not requiring large, labelled
datasets. Such self-supervised methods require high-quality salient features
and consequently suffer from severe performance drop for indoor scenes, where
low-textured regions dominant in the scenes are almost indiscriminative. To
address the issue, we propose a self-supervised indoor monocular depth
estimation framework called $\mathrm{F^2Depth}$. A self-supervised optical flow
estimation network is introduced to supervise depth learning. To improve
optical flow estimation performance in low-textured areas, only some patches of
points with more discriminative features are adopted for finetuning based on
our well-designed patch-based photometric loss. The finetuned optical flow
estimation network generates high-accuracy optical flow as a supervisory signal
for depth estimation. Correspondingly, an optical flow consistency loss is
designed. Multi-scale feature maps produced by finetuned optical flow
estimation network perform warping to compute feature map synthesis loss as
another supervisory signal for depth learning. Experimental results on the NYU
Depth V2 dataset demonstrate the effectiveness of the framework and our
proposed losses. To evaluate the generalization ability of our
$\mathrm{F^2Depth}$, we collect a Campus Indoor depth dataset composed of
approximately 1500 points selected from 99 images in 18 scenes. Zero-shot
generalization experiments on 7-Scenes dataset and Campus Indoor achieve
$\delta_1$ accuracy of 75.8% and 76.0% respectively. The accuracy results show
that our model can generalize well to monocular images captured in unknown
indoor scenes.
