  This paper focuses on self-supervised monocular depth estimation in dynamic
scenes trained on monocular videos. Existing methods jointly estimate
pixel-wise depth and motion, relying mainly on an image reconstruction loss.
Dynamic regions1 remain a critical challenge for these methods due to the
inherent ambiguity in depth and motion estimation, resulting in inaccurate
depth estimation. This paper proposes a self-supervised training framework
exploiting pseudo depth labels for dynamic regions from training data. The key
contribution of our framework is to decouple depth estimation for static and
dynamic regions of images in the training data. We start with an unsupervised
depth estimation approach, which provides reliable depth estimates for static
regions and motion cues for dynamic regions and allows us to extract moving
object information at the instance level. In the next stage, we use an object
network to estimate the depth of those moving objects assuming rigid motions.
Then, we propose a new scale alignment module to address the scale ambiguity
between estimated depths for static and dynamic regions. We can then use the
depth labels generated to train an end-to-end depth estimation network and
improve its performance. Extensive experiments on the Cityscapes and KITTI
datasets show that our self-training strategy consistently outperforms existing
self/unsupervised depth estimation methods.
