  Self-supervised monocular depth estimation has received much attention
recently in computer vision. Most of the existing works in literature aggregate
multi-scale features for depth prediction via either straightforward
concatenation or element-wise addition, however, such feature aggregation
operations generally neglect the contextual consistency between multi-scale
features. Addressing this problem, we propose the Self-Distilled Feature
Aggregation (SDFA) module for simultaneously aggregating a pair of low-scale
and high-scale features and maintaining their contextual consistency. The SDFA
employs three branches to learn three feature offset maps respectively: one
offset map for refining the input low-scale feature and the other two for
refining the input high-scale feature under a designed self-distillation
manner. Then, we propose an SDFA-based network for self-supervised monocular
depth estimation, and design a self-distilled training strategy to train the
proposed network with the SDFA module. Experimental results on the KITTI
dataset demonstrate that the proposed method outperforms the comparative
state-of-the-art methods in most cases. The code is available at
https://github.com/ZM-Zhou/SDFA-Net_pytorch.
