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Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video. Unsupervised learning removes the need for separate supervisory signals (depth or ego-motion ground truth, or multi-view video). Prior work in unsupervised depth learning uses pixel-wise or gradient-based losses, which only consider pixels in small local neighborhoods. Our main contribution is to explicitly consider the inferred 3D geometry of the whole scene, and enforce consistency of the
doi:10.1109/cvpr.2018.00594
dblp:conf/cvpr/MahjourianWA18
fatcat:vvjhfpn4qvdkffujq747pxfk4e