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SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that
doi:10.1109/cvpr.2019.01004
dblp:conf/cvpr/MengLRSGJBB19
fatcat:c46o7rnvordzhnf62b35i3dhmy