  Self-supervised methods have showed promising results on depth estimation
task. However, previous methods estimate the target depth map and camera
ego-motion simultaneously, underusing multi-frame correlation information and
ignoring the motion of dynamic objects. In this paper, we propose a novel
Dyna-Depthformer framework, which predicts scene depth and 3D motion field
jointly and aggregates multi-frame information with transformer. Our
contributions are two-fold. First, we leverage multi-view correlation through a
series of self- and cross-attention layers in order to obtain enhanced depth
feature representation. Specifically, we use the perspective transformation to
acquire the initial reference point, and use deformable attention to reduce the
computational cost. Second, we propose a warping-based Motion Network to
estimate the motion field of dynamic objects without using semantic prior. To
improve the motion field predictions, we propose an iterative optimization
strategy, together with a sparsity-regularized loss. The entire pipeline
achieves end-to-end self-supervised training by constructing a minimum
reprojection loss. Extensive experiments on the KITTI and Cityscapes benchmarks
demonstrate the effectiveness of our method and show that our method
outperforms state-of-the-art algorithms.
