  Self-supervised monocular methods can efficiently learn depth information of
weakly textured surfaces or reflective objects. However, the depth accuracy is
limited due to the inherent ambiguity in monocular geometric modeling. In
contrast, multi-frame depth estimation methods improve the depth accuracy
thanks to the success of Multi-View Stereo (MVS), which directly makes use of
geometric constraints. Unfortunately, MVS often suffers from texture-less
regions, non-Lambertian surfaces, and moving objects, especially in real-world
video sequences without known camera motion and depth supervision. Therefore,
we propose MOVEDepth, which exploits the MOnocular cues and VElocity guidance
to improve multi-frame Depth learning. Unlike existing methods that enforce
consistency between MVS depth and monocular depth, MOVEDepth boosts multi-frame
depth learning by directly addressing the inherent problems of MVS. The key of
our approach is to utilize monocular depth as a geometric priority to construct
MVS cost volume, and adjust depth candidates of cost volume under the guidance
of predicted camera velocity. We further fuse monocular depth and MVS depth by
learning uncertainty in the cost volume, which results in a robust depth
estimation against ambiguity in multi-view geometry. Extensive experiments show
MOVEDepth achieves state-of-the-art performance: Compared with Monodepth2 and
PackNet, our method relatively improves the depth accuracy by 20\% and 19.8\%
on the KITTI benchmark. MOVEDepth also generalizes to the more challenging DDAD
benchmark, relatively outperforming ManyDepth by 7.2\%. The code is available
at https://github.com/JeffWang987/MOVEDepth.
