Super-resolution algorithms reconstruct high-resolution images from low-resolution input images. For this purpose, multi-sensor super-resolution describes a technique to reconstruct high-resolution images from low-resolution data for one modality under the guidance of another modality. In our work, we investigate this concept for hybrid range imaging to super-resolve low-resolution 3-D range data that is fused with complementary photometric information (RGB data). In order to improve robustness, different computational steps of the super-resolution algorithm can be applied on the guidance data instead of using the low-resolution input images directly. This is beneficial e.g. for motion estimation required for a multi-frame super-resolution reconstruction as higher accuracy can be achieved on the guidance images.

We plan to include more datasets to consider different applications of our method in the future. The use of this data is free but please cite our corresponding paper if you would like to use it in your next publication.