  Single image depth estimation (SIDE) plays a crucial role in 3D computer
vision. In this paper, we propose a two-stage robust SIDE framework that can
perform blind SIDE for both indoor and outdoor scenes. At the first stage, the
scene understanding module will categorize the RGB image into different
depth-ranges. We introduce two different scene understanding modules based on
scene classification and coarse depth estimation respectively. At the second
stage, SIDE networks trained by the images of specific depth-range are applied
to obtain an accurate depth map. In order to improve the accuracy, we further
design a multi-task encoding-decoding SIDE network DS-SIDENet based on
depthwise separable convolutions. DS-SIDENet is optimized to minimize both
depth classification and depth regression losses. This improves the accuracy
compared to a single-task SIDE network. Experimental results demonstrate that
training DS-SIDENet on an individual dataset such as NYU achieves competitive
performance to the state-of-art methods with much better efficiency. Ours
proposed robust SIDE framework also shows good performance for the ScanNet
indoor images and KITTI outdoor images simultaneously. It achieves the top
performance compared to the Robust Vision Challenge (ROB) 2018 submissions.
