  We propose a novel geometric and photometric 3D mapping pipeline for accurate
and real-time scene reconstruction from monocular images. To achieve this, we
leverage recent advances in dense monocular SLAM and real-time hierarchical
volumetric neural radiance fields. Our insight is that dense monocular SLAM
provides the right information to fit a neural radiance field of the scene in
real-time, by providing accurate pose estimates and depth-maps with associated
uncertainty. With our proposed uncertainty-based depth loss, we achieve not
only good photometric accuracy, but also great geometric accuracy. In fact, our
proposed pipeline achieves better geometric and photometric accuracy than
competing approaches (up to 179% better PSNR and 86% better L1 depth), while
working in real-time and using only monocular images.
