UNSUPERVISED MONOCULAR DEPTH ESTIMATION WITH CLEAR BOUNDARIES

Yihan Hu, Heng Luo, Yifeng Geng

Abstract:

Unsupervised monocular depth estimation has made great progress after deep
learning is involved. Training with binocular stereo images is considered as a
good option as the data can be easily obtained. However, the depth or disparity
prediction results show poor performance for the object boundaries. The main
reason is related to the handling of occlusion areas during the training. In this paper,
we propose a novel method to overcome this issue. Exploiting disparity maps
property, we generate an occlusion mask to block the back-propagation of the occlusion
areas during image warping. We also design new networks with flipped
stereo images to induce the networks to learn occluded boundaries. It shows that
our method achieves clearer boundaries and better evaluation results on KITTI
driving dataset and Virtual KITTI dataset.

Keywords: monocular depth estimation, unsupervised learning, image warping

TL;DR: This paper propose a mask method which solves the previous blurred results of unsupervised monocular depth estimation caused by occlusion