  Depth maps obtained by commercial depth sensors are always in low-resolution,
making it difficult to be used in various computer vision tasks. Thus, depth
map super-resolution (SR) is a practical and valuable task, which upscales the
depth map into high-resolution (HR) space. However, limited by the lack of
real-world paired low-resolution (LR) and HR depth maps, most existing methods
use downsampling to obtain paired training samples. To this end, we first
construct a large-scale dataset named "RGB-D-D", which can greatly promote the
study of depth map SR and even more depth-related real-world tasks. The "D-D"
in our dataset represents the paired LR and HR depth maps captured from mobile
phone and Lucid Helios respectively ranging from indoor scenes to challenging
outdoor scenes. Besides, we provide a fast depth map super-resolution (FDSR)
baseline, in which the high-frequency component adaptively decomposed from RGB
image to guide the depth map SR. Extensive experiments on existing public
datasets demonstrate the effectiveness and efficiency of our network compared
with the state-of-the-art methods. Moreover, for the real-world LR depth maps,
our algorithm can produce more accurate HR depth maps with clearer boundaries
and to some extent correct the depth value errors.
