  Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from
low-resolution (LR) one, where RGB image is often used to promote this task.
Recent image guided DSR approaches mainly focus on spatial domain to rebuild
depth structure. However, since the structure of LR depth is usually blurry,
only considering spatial domain is not very sufficient to acquire satisfactory
results. In this paper, we propose structure guided network (SGNet), a method
that pays more attention to gradient and frequency domains, both of which have
the inherent ability to capture high-frequency structure. Specifically, we
first introduce the gradient calibration module (GCM), which employs the
accurate gradient prior of RGB to sharpen the LR depth structure. Then we
present the Frequency Awareness Module (FAM) that recursively conducts multiple
spectrum differencing blocks (SDB), each of which propagates the precise
high-frequency components of RGB into the LR depth. Extensive experimental
results on both real and synthetic datasets demonstrate the superiority of our
SGNet, reaching the state-of-the-art. Codes and pre-trained models are
available at https://github.com/yanzq95/SGNet.
