DeepRelativeFusion: Dense Monocular SLAM using Single-Image Relative Depth Prediction

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 combine the keyframe pose-graph with the densified keyframe depth maps to reconstruct the scene. 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. Moreover, we show that the relative depth maps can be corrected and are sufficiently accurate to be used as priors for the densification. To demonstrate the generalizability of relative depth prediction, we illustrate qualitatively the dense reconstruction on two outdoor sequences. Our system also outperforms the state-of-the-art dense SLAM systems quantitatively in dense reconstruction accuracy by a large margin.