  In this paper, we proposed a new deep learning based dense monocular SLAM
method. Compared to existing methods, the proposed framework constructs a dense
3D model via a sparse to dense mapping using learned surface normals. With
single view learned depth estimation as prior for monocular visual odometry, we
obtain both accurate positioning and high quality depth reconstruction. The
depth and normal are predicted by a single network trained in a tightly coupled
manner.Experimental results show that our method significantly improves the
performance of visual tracking and depth prediction in comparison to the
state-of-the-art in deep monocular dense SLAM.
