  In this paper, we propose a dense monocular SLAM system, named
DeepRelativeFusion, that is capable to recover a globally consistent 3D
structure. To this end, we use a visual SLAM algorithm to reliably recover the
camera poses and semi-dense depth maps of the keyframes, and then use relative
depth prediction to densify the semi-dense depth maps and refine the keyframe
pose-graph. To improve the semi-dense depth maps, we propose an adaptive
filtering scheme, which is a structure-preserving weighted average smoothing
filter that takes into account the pixel intensity and depth of the
neighbouring pixels, yielding substantial reconstruction accuracy gain in
densification. To perform densification, we introduce two incremental
improvements upon the energy minimization framework proposed by DeepFusion: (1)
an improved cost function, and (2) the use of single-image relative depth
prediction. After densification, we update the keyframes with two-view
consistent optimized semi-dense and dense depth maps to improve pose-graph
optimization, providing a feedback loop to refine the keyframe poses for
accurate scene reconstruction. Our system outperforms the state-of-the-art
dense SLAM systems quantitatively in dense reconstruction accuracy by a large
margin.
