  This paper introduces a novel deep framework for dense 3D reconstruction from
multiple image frames, leveraging a sparse set of depth measurements gathered
jointly with image acquisition. Given a deep multi-view stereo network, our
framework uses sparse depth hints to guide the neural network by modulating the
plane-sweep cost volume built during the forward step, enabling us to infer
constantly much more accurate depth maps. Moreover, since multiple viewpoints
can provide additional depth measurements, we propose a multi-view guidance
strategy that increases the density of the sparse points used to guide the
network, thus leading to even more accurate results. We evaluate our Multi-View
Guided framework within a variety of state-of-the-art deep multi-view stereo
networks, demonstrating its effectiveness at improving the results achieved by
each of them on BlendedMVG and DTU datasets.
