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We propose GeoNet, a jointly unsupervised learning framework for monocular depth, optical flow and ego-motion estimation from videos.
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Flownet: Learning optical flow with convolutional networks
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Optical flow with semantic segmentation and localized layers
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Efficient non-consecutive feature tracking for robust structure-from-motion
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Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios?
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ORB-SLAM: a versatile and accurate monocular SLAM system
R. Mur-Artal, J. D. Tardós, J. M. M. Montiel, and D. Gálvez-López · 2015
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Epicflow: Edge-preserving interpolation of correspondences for optical flow
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Optimizing the viewing graph for structure-from-motion
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Going deeper with convolutions
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3d scene flow estimation with a piecewise rigid scene model
C. Vogel, K. Schindler, and S. Roth · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
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C. Godard, O. Mac Aodha, and G. J. Brostow · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
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Geometric loss functions for camera pose regression with deep learning
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Unsupervised deep learning for optical flow estimation
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Fast multi-frame stereo scene flow with motion segmentation
T. Taniai, S. N. Sinha, and Y. Sato · 2017
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Demon: Depth and motion network for learning monocular stereo
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Optical flow in mostly rigid scenes
J. Wulff, L. Sevilla-Lara, and M. J. Black · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe · 2017
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UnFlow: Unsupervised learning of optical flow with a bidirectional census loss
S. Meister, J. Hur, and S. Roth · 2018
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