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We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving.
Non-parametric local transforms for computing visual correspondence
R.Zabih, Woodfill, J.: · 1994
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In Defence of the Eight-Point Algorithm
Hartley, R.: · 1997
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Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods
A.Bruhn, J.Weickert, C.Schnoerr: · 2004
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Distinctive image features from scale-invariant keypoints
Lowe, D.: · 2004
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Highly accurate optical flow computation with theoretically justified warping
Papenberg, N., Bruhn, A., Brox, T., Didas, S., Weickert, J.: · 2006
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Two-View Multibody Structure from Motion
Vidal, R., Ma, Y., Soatto, S., Sastry, S.: · 2006
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Stereo Processing by Semigloabl Matching and Mutual Information
Hirschmuller, H.: · 2008
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FusionFlow: Discrete-Continuous Optimization for Optical Flow Estimation
Lempitsky, V., Roth, S., Rother, C.: · 2008
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Optical Flow Estimation on Coarse-to-Fine Region-Trees using Discrete Optimization
C.Lei, Y.H.Yang: · 2009
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Secrets of Optical Flow Estimation and Their Principles
D.Sun, S.Roth, M.J.Black: · 2010
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DAISY: An Efficient Dense Descriptor Applied to Wide Baseline Stereo
Tola, E., Lepetit, V., Fua, P.: · 2010
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Simultaneous Multi-Body Stereo and Segmentation
Zhang, G., Jia, J., Bao, H.: · 2011
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Dense Multibody Motion Estimation and Reconstruction from a Handheld Camera
Roussos, A., Russell, C., Garg, R., Agapito, L.: · 2012
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Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
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3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model
Fidler, S., Dickinson, S., Urtasun, R.: · 2012
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A quantitative analysis of current practices in optical flow estimation and the principles behind them
Sun, D., Roth, S., Black, M.: · 2013
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Robust Monocular Epipolar Flow Estimation
Yamaguchi, K., Mcallester, D., Urtasun, R.: · 2013
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DeepFlow: Large Displacement Optical Flow with Deep Matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: · 2013
Cited alongside, same era.
DeepFlow: Large displacement optical flow with deep matching
Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: · 2013
Cited alongside, same era.
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation
K.Yamaguchi, D.McAllester, R.Urtasun: · 2014
Dense, Accurate Optical Flow Estimation with Piecewise Parametric Model
Yang, J., Li, H.: · 2015
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3D Scene Flow Estimation
Vogel, C., Schindler, K., Roth, S.: · 2015
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Computing the stereo matching cost with a convolutional neural network
Zbontar, J., LeCun, Y.: · 2015
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A deep visual correspondence embedding model for stereo matching costs
Chen, Z., Sun, X., Wang, L., Yu, Y., Huang, C.: · 2015
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Learning to compare image patches via convolutional neural networks
Zagoruyko, S., Komodakis, N.: · 2015
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FlowNet: Learning Optical Flow with Convolutional Networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P., Cremers, D., Brox, T.: · 2015
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Cited alongside, same era.
DAISY filter flow: A generalized discrete approach to dense correspondences
Yang, H., Lin, W., Lu, J.: · 2014
Cited alongside, same era.
Fast edge-preserving PatchMatch for large displacement optical flow
Bao, L., Yang, Q., Jin, H.: · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2014
Cited alongside, same era.
EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow
Revaud, J., Weinzaepfel, P., Harchaoui, Z., C.Schmid: · 2015
Cited alongside, same era.
Object Scene Flow for Autonomous Vehicles
Menze, M., Geiger, A.: · 2015
Cited alongside, same era.
An iterative image registration technique with an application to stereo vision
Lucas, B., Kanade, T.: · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Fast r-cnn
Girshick, R.: · 2015
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Optical Flow with Semantic Segmentation and Localized Layers
Sevilla-Lara, L., Sun, D., Jampani, V., Black, M.: · 2016
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Efficient deep learning for stereo matching
Luo, W., Schwing, A., Urtasun, R.: · 2016
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Instance-Level Segmentation with Deep Densely Connected MRFs
Zhang, Z., Fidler, S., Urtasun, R.: · 2016
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Monocular 3D Object Detection for Autonomous Driving
Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., Urtasun, R.: · 2016
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End-to-end instance segmentation and counting with recurrent attention
Ren, M., Zemel, R.: · 2016
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