  Managing the dynamic regions in the photometric loss formulation has been a
main issue for handling the self-supervised depth estimation problem. Most
previous methods have alleviated this issue by removing the dynamic regions in
the photometric loss formulation based on the masks estimated from another
module, making it difficult to fully utilize the training images. In this
paper, to handle this problem, we propose an isometric self-sample-based
learning (ISSL) method to fully utilize the training images in a simple yet
effective way. The proposed method provides additional supervision during
training using self-generated images that comply with pure static scene
assumption. Specifically, the isometric self-sample generator synthesizes
self-samples for each training image by applying random rigid transformations
on the estimated depth. Thus both the generated self-samples and the
corresponding training image always follow the static scene assumption. We show
that plugging our ISSL module into several existing models consistently
improves the performance by a large margin. In addition, it also boosts the
depth accuracy over different types of scene, i.e., outdoor scenes (KITTI and
Make3D) and indoor scene (NYUv2), validating its high effectiveness.
