Epipolar Geometry based Learning of Multi-view Depth and Ego-Motion from Monocular Sequences
Vignesh Prasad, Dipanjan Das, Brojeshwar Bhowmick · 2018
Deep approaches to predict monocular depth and ego-motion have grown in recent years due to their ability to produce dense depth from monocular images. The main idea behind them is to optimize the photometric consistency over image sequences by warping one view into another, similar to direct visual odometry methods. One major drawback is that these methods infer depth from a single view, which might not effectively capture the relation between pixels. Moreover, simply minimizing the photometric loss does not ensure proper pixel correspondences, which is a key factor for accurate depth and pose estimations.