From-Ground-To-Objects: Coarse-to-Fine Self-supervised Monocular Depth
  Estimation of Dynamic Objects with Ground Contact Prior
Jaeho Moon Juan Luis Gonzalez Bello Byeongjun Kwon Munchurl Kim
Self-supervised monocular depth estimation (DE) is an approach to learning
depth without costly depth ground truths. However, it often struggles with
moving objects that violate the static scene assumption during training. To
address this issue, we introduce a coarse-to-fine training strategy leveraging
the ground contacting prior based on the observation that most moving objects
in outdoor scenes contact the ground. In the coarse training stage, we exclude
the objects in dynamic classes from the reprojection loss calculation to avoid
inaccurate depth learning. To provide precise supervision on the depth of the
objects, we present a novel Ground-contacting-prior Disparity Smoothness Loss
(GDS-Loss) that encourages a DE network to align the depth of the objects with
their ground-contacting points. Subsequently, in the fine training stage, we
refine the DE network to learn the detailed depth of the objects from the
reprojection loss, while ensuring accurate DE on the moving object regions by
employing our regularization loss with a cost-volume-based weighting factor.
Our overall coarse-to-fine training strategy can easily be integrated with
existing DE methods without any modifications, significantly enhancing DE
performance on challenging Cityscapes and KITTI datasets, especially in the
moving object regions.