In self-supervised monocular depth estimation, the depth discontinuity and
motion objects' artifacts are still challenging problems. Existing
self-supervised methods usually utilize a single view to train the depth
estimation network. Compared with static views, abundant dynamic properties
between video frames are beneficial to refined depth estimation, especially for
dynamic objects. In this work, we propose a novel self-supervised joint
learning framework for depth estimation using consecutive frames from monocular
and stereo videos. The main idea is using an implicit depth cue extractor which
leverages dynamic and static cues to generate useful depth proposals. These
cues can predict distinguishable motion contours and geometric scene
structures. Furthermore, a new high-dimensional attention module is introduced
to extract clear global transformation, which effectively suppresses
uncertainty of local descriptors in high-dimensional space, resulting in a more
reliable optimization in learning framework. Experiments demonstrate that the
proposed framework outperforms the state-of-the-art(SOTA) on KITTI and Make3D
datasets.

本研究提出了一种利用单目和立体相机视频中的连续帧进行深度估计的自监督联合学习框架，其中使用了隐式深度线索提取器来利用动态和静态线索产生有用的深度建议，并通过引入高维注意力模块来提取清晰的全局变换。实验证明，所提出的框架在 KITTI 和 Make3D 数据集上优于现有技术。