  In this paper, we address the problem of fusing monocular depth estimation
with a conventional multi-view stereo or SLAM to exploit the best of both
worlds, that is, the accurate dense depth of the first one and lightweightness
of the second one. More specifically, we use a conventional pipeline to produce
a sparse 3D point cloud that is fed to a monocular depth estimation network to
enhance its performance. In this way, we can achieve accuracy similar to
multi-view stereo with a considerably smaller number of weights. We also show
that even as few as 32 points is sufficient to outperform the best monocular
depth estimation methods, and around 200 points to gain full advantage of the
additional information. Moreover, we demonstrate the efficacy of our approach
by integrating it with a SLAM system built-in on mobile devices.
