The exploration of mutual-benefit cross-domains has shown great potential
toward accurate self-supervised depth estimation. In this work, we revisit
feature fusion between depth and semantic information and propose an efficient
local adaptive attention method for geometric aware representation enhancement.
Instead of building global connections or deforming attention across the
feature space without restraint, we bound the spatial interaction within a
learnable region of interest. In particular, we leverage geometric cues from
semantic information to learn local adaptive bounding boxes to guide
unsupervised feature aggregation. The local areas preclude most irrelevant
reference points from attention space, yielding more selective feature learning
and faster convergence. We naturally extend the paradigm into a multi-head and
hierarchic way to enable the information distillation in different semantic
levels and improve the feature discriminative ability for fine-grained depth
estimation. Extensive experiments on the KITTI dataset show that our proposed
method establishes a new state-of-the-art in self-supervised monocular depth
estimation task, demonstrating the effectiveness of our approach over former
Transformer variants.

通过基于语义信息的几何启示本文提出了一种高效的本地自适应注意方法来增强几何意识的表示，使得其在 KITTI 数据集上建立了新的最前沿，证明了本方法在自监督单目深度估计任务上的有效性。