Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution

Understand

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.