  Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image
processing, aims to upsample low-resolution (LR) depth maps with additional
information involved in high-resolution (HR) RGB images from the same scene.
The critical step of this task is to effectively extract domain-shared and
domain-private RGB/depth features. In addition, three detailed issues, namely
blurry edges, noisy surfaces, and over-transferred RGB texture, need to be
addressed. In this paper, we propose the Spherical Space feature Decomposition
Network (SSDNet) to solve the above issues. To better model cross-modality
features, Restormer block-based RGB/depth encoders are employed for extracting
local-global features. Then, the extracted features are mapped to the spherical
space to complete the separation of private features and the alignment of
shared features. Shared features of RGB are fused with the depth features to
complete the GDSR task. Subsequently, a spherical contrast refinement (SCR)
module is proposed to further address the detail issues. Patches that are
classified according to imperfect categories are input into the SCR module,
where the patch features are pulled closer to the ground truth and pushed away
from the corresponding imperfect samples in the spherical feature space via
contrastive learning. Extensive experiments demonstrate that our method can
achieve state-of-the-art results on four test datasets, as well as successfully
generalize to real-world scenes. The code is available at
\url{https://github.com/Zhaozixiang1228/GDSR-SSDNet}.
