  Self-supervised monocular depth estimation has gathered notable interest
since it can liberate training from dependency on depth annotations. In
monocular video training case, recent methods only conduct view synthesis
between existing camera views, leading to insufficient guidance. To tackle
this, we try to synthesize more virtual camera views by flow-based video frame
interpolation (VFI), termed as temporal augmentation. For multi-frame
inference, to sidestep the problem of dynamic objects encountered by explicit
geometry-based methods like ManyDepth, we return to the feature fusion paradigm
and design a VFI-assisted multi-frame fusion module to align and aggregate
multi-frame features, using motion and occlusion information obtained by the
flow-based VFI model. Finally, we construct a unified self-supervised learning
framework, named Mono-ViFI, to bilaterally connect single- and multi-frame
depth. In this framework, spatial data augmentation through image affine
transformation is incorporated for data diversity, along with a triplet depth
consistency loss for regularization. The single- and multi-frame models can
share weights, making our framework compact and memory-efficient. Extensive
experiments demonstrate that our method can bring significant improvements to
current advanced architectures. Source code is available at
https://github.com/LiuJF1226/Mono-ViFI.
