  Multi-frame methods improve monocular depth estimation over single-frame
approaches by aggregating spatial-temporal information via feature matching.
However, the spatial-temporal feature leads to accuracy degradation in dynamic
scenes. To enhance the performance, recent methods tend to propose complex
architectures for feature matching and dynamic scenes. In this paper, we show
that a simple learning framework, together with designed feature augmentation,
leads to superior performance. (1) A novel dynamic objects detecting method
with geometry explainability is proposed. The detected dynamic objects are
excluded during training, which guarantees the static environment assumption
and relieves the accuracy degradation problem of the multi-frame depth
estimation. (2) Multi-scale feature fusion is proposed for feature matching in
the multi-frame depth network, which improves feature matching, especially
between frames with large camera motion. (3) The robust knowledge distillation
with a robust teacher network and reliability guarantee is proposed, which
improves the multi-frame depth estimation without computation complexity
increase during the test. The experiments show that our proposed methods
achieve great performance improvement on the multi-frame depth estimation.
