Self-supervised multi-frame methods have currently achieved promising results
in depth estimation. However, these methods often suffer from mismatch problems
due to the moving objects, which break the static assumption. Additionally,
unfairness can occur when calculating photometric errors in high-freq or
low-texture regions of the images. To address these issues, existing approaches
use additional semantic priori black-box networks to separate moving objects
and improve the model only at the loss level. Therefore, we propose FlowDepth,
where a Dynamic Motion Flow Module (DMFM) decouples the optical flow by a
mechanism-based approach and warps the dynamic regions thus solving the
mismatch problem. For the unfairness of photometric errors caused by high-freq
and low-texture regions, we use Depth-Cue-Aware Blur (DCABlur) and Cost-Volume
sparsity loss respectively at the input and the loss level to solve the
problem. Experimental results on the KITTI and Cityscapes datasets show that
our method outperforms the state-of-the-art methods.

通过动态的运动流模块及深度感知模糊和成本体积稀疏损失，在自我监督多帧方法中解决了深度估计中的运动物体、光流和光度误差问题，并在实验中表现出超越其他方法的性能。