  We present a novel method to reconstruct 3D scenes from images by leveraging
deep dense monocular SLAM and fast uncertainty propagation. The proposed
approach is able to 3D reconstruct scenes densely, accurately, and in real-time
while being robust to extremely noisy depth estimates coming from dense
monocular SLAM. Differently from previous approaches, that either use ad-hoc
depth filters, or that estimate the depth uncertainty from RGB-D cameras'
sensor models, our probabilistic depth uncertainty derives directly from the
information matrix of the underlying bundle adjustment problem in SLAM. We show
that the resulting depth uncertainty provides an excellent signal to weight the
depth-maps for volumetric fusion. Without our depth uncertainty, the resulting
mesh is noisy and with artifacts, while our approach generates an accurate 3D
mesh with significantly fewer artifacts. We provide results on the challenging
Euroc dataset, and show that our approach achieves 92% better accuracy than
directly fusing depths from monocular SLAM, and up to 90% improvements compared
to the best competing approach.
