  Depth information is the foundation of perception, essential for autonomous
driving, robotics, and other source-constrained applications. Promptly
obtaining accurate and efficient depth information allows for a rapid response
in dynamic environments. Sensor-based methods using LIDAR and RADAR obtain high
precision at the cost of high power consumption, price, and volume. While due
to advances in deep learning, vision-based approaches have recently received
much attention and can overcome these drawbacks. In this work, we explore an
extreme scenario in vision-based settings: estimate a depth map from one
monocular image severely plagued by grid artifacts and blurry edges. To address
this scenario, We first design a convolutional attention mechanism block (CAMB)
which consists of channel attention and spatial attention sequentially and
insert these CAMBs into skip connections. As a result, our novel approach can
find the focus of current image with minimal overhead and avoid losses of depth
features. Next, by combining the depth value, the gradients of X axis, Y axis
and diagonal directions, and the structural similarity index measure (SSIM), we
propose our novel loss function. Moreover, we utilize pixel blocks to
accelerate the computation of the loss function. Finally, we show, through
comprehensive experiments on two large-scale image datasets, i.e. KITTI and
NYU-V2, that our method outperforms several representative baselines.
