  Today, Multi-View Stereo techniques are able to reconstruct robust and
detailed 3D models, especially when starting from high-resolution images.
However, there are cases in which the resolution of input images is relatively
low, for instance, when dealing with old photos, or when hardware constrains
the amount of data that can be acquired. In this paper, we investigate if, how,
and how much increasing the resolution of such input images through
Super-Resolution techniques reflects in quality improvements of the
reconstructed 3D models, despite the artifacts that sometimes this may
generate. We show that applying a Super-Resolution step before recovering the
depth maps in most cases leads to a better 3D model both in the case of
PatchMatch-based and deep-learning-based algorithms. The use of
Super-Resolution improves especially the completeness of reconstructed models
and turns out to be particularly effective in the case of textured scenes.
