  Multi-frame depth estimation generally achieves high accuracy relying on the
multi-view geometric consistency. When applied in dynamic scenes, e.g.,
autonomous driving, this consistency is usually violated in the dynamic areas,
leading to corrupted estimations. Many multi-frame methods handle dynamic areas
by identifying them with explicit masks and compensating the multi-view cues
with monocular cues represented as local monocular depth or features. The
improvements are limited due to the uncontrolled quality of the masks and the
underutilized benefits of the fusion of the two types of cues. In this paper,
we propose a novel method to learn to fuse the multi-view and monocular cues
encoded as volumes without needing the heuristically crafted masks. As unveiled
in our analyses, the multi-view cues capture more accurate geometric
information in static areas, and the monocular cues capture more useful
contexts in dynamic areas. To let the geometric perception learned from
multi-view cues in static areas propagate to the monocular representation in
dynamic areas and let monocular cues enhance the representation of multi-view
cost volume, we propose a cross-cue fusion (CCF) module, which includes the
cross-cue attention (CCA) to encode the spatially non-local relative
intra-relations from each source to enhance the representation of the other.
Experiments on real-world datasets prove the significant effectiveness and
generalization ability of the proposed method.
