  Self-supervised monocular depth estimation methods generally suffer the
occlusion fading issue due to the lack of supervision by the per pixel ground
truth. Although a post-processing method was proposed by Godard et. al. to
reduce the occlusion fading, the compensated results have a severe halo effect.
In this paper, we propose a novel Edge-Guided post-processing to reduce the
occlusion fading issue for self-supervised monocular depth estimation. We
further introduce Atrous Spatial Pyramid Pooling (ASPP) into the network to
reduce the computational costs and improve the inference performance. The
proposed ASPP-based network is lighter, faster, and better than current
commonly used depth estimation networks. This light-weight network only needs
8.1 million parameters and can achieve up to 40 frames per second for
$256\times512$ input in the inference stage using a single nVIDIA GTX1080 GPU.
The proposed network also outperforms the current state-of-the-art on the KITTI
benchmarks. The ASPP-based network and Edge-Guided post-processing produce
better results either quantitatively and qualitatively than the competitors.
