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
this https URL

通过多视图几何从单目序列中获取粗略相机姿态以解决室内场景中自监督单目深度估计的挑战，通过旋转和平移 / 缩放优化来改进姿态，通过全局推理的视觉变换器和迭代的自蒸馏机制软化低纹理效果，实验结果显示了框架中每个组件的有效性，并提供了室内自监督单目深度估计的新的技术水平以及出色的泛化能力。