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.

本文提出了一种新的深度图像超分辨率方法，利用移动手机和 Lucid Helios 采集的配对 LR 和 HR 深度图构建了一个名为 RGB-D-D 的大规模数据集，并提供了一个快速深度图超分辨率（FDSR）基准线，与现有公共数据集上的基准方法相比，我们的网络在效果和效率上都有很大的提升，并且可以处理真实世界中的 LR 深度图以产生更准确，清晰的边界，并在一定程度上修正深度值错误。