Interactive State Space Model with Cross-Modal Local Scanning for Depth Super-Resolution
Researchers have introduced a new framework for guided depth super-resolution that utilizes an Interactive State Space Model. This approach aims to efficiently create high-resolution depth maps from low-resolution inputs, using RGB images as guidance. The model incorporates a cross-modal local scanning mechanism to enable detailed semantic interactions between RGB and depth features, leveraging the Mamba architecture for linear complexity. Experiments indicate that this method achieves competitive results compared to existing state-of-the-art techniques. AI
IMPACT Introduces a novel approach for depth super-resolution, potentially improving efficiency and accuracy in computer vision tasks.