  This paper tackles the challenges of self-supervised monocular depth
estimation in indoor scenes caused by large rotation between frames and low
texture. We ease the learning process by obtaining coarse camera poses from
monocular sequences through multi-view geometry to deal with the former.
However, we found that limited by the scale ambiguity across different scenes
in the training dataset, a na\"ive introduction of geometric coarse poses
cannot play a positive role in performance improvement, which is
counter-intuitive. To address this problem, we propose to refine those poses
during training through rotation and translation/scale optimization. To soften
the effect of the low texture, we combine the global reasoning of vision
transformers with an overfitting-aware, iterative self-distillation mechanism,
providing more accurate depth guidance coming from the network itself.
Experiments on NYUv2, ScanNet, 7scenes, and KITTI datasets support the
effectiveness of each component in our framework, which sets a new
state-of-the-art for indoor self-supervised monocular depth estimation, as well
as outstanding generalization ability. Code and models are available at
https://github.com/zxcqlf/GasMono
