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Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications.
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U. Franke, C. Rabe, H. Badino, and S. Gehrig · 2005
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Three-dimensional scene flow
S. Vedula, P. Rander, R. Collins, and T. Kanade · 2005
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F. Huguet and F. Devernay · 2007
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A. Wedel, C. Rabe, T. Vaudrey, T. Brox, U. Franke, and D. Cremers · 2008
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R. B. Rusu, N. Blodow, and M. Beetz · 2009
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F. Tombari, S. Salti, and L. di Stefano · 2010
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L. Valgaerts, A. Bruhn, H. Zimmer, J. Weickert, C. Stoll, and C. Theobalt · 2010
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An integrated probabilistic model for scan-matching, moving object detection and motion estimation
J. van de Ven, F. Ramos, and G. D. Tipaldi · 2010
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Modeling and tracking the driving environment with a particle-based occupancy grid
R. Danescu, F. Oniga, and S. Nedevschi · 2011
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Sparse scene flow segmentation for moving object detection in urban environments
P. Lenz, J. Ziegler, A. Geiger, and M. Roser · 2011
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3D scene flow estimation with a rigid motion prior
C. Vogel, K. Schindler, and S. Roth · 2011
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Stereoscopic scene flow computation for 3D motion understanding
A. Wedel, T. Brox, T. Vaudrey, C. Rabe, U. Franke, and D. Cremers · 2011
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3d object detection and viewpoint estimation with a deformable 3d cuboid model
S. Fidler, S. Dickinson, and R. Urtasun · 2012
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Are we ready for autonomous driving? The KITTI vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
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Multi-view scene flow estimation: A view centered variational approach
T. Basha, Y. Moses, and N. Kiryati · 2013
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RGB-D flow: Dense 3D motion estimation using color and depth
E. Herbst, X. Ren, and D. Fox · 2013
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Joint self-localization and tracking of generic objects in 3d range data
F. Moosmann and C. Stiller · 2013
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SphereFlow: 6 DoF scene flow from RGB-D pairs
M. Hornacek, A. Fitzgibbon, and C. Rother · 2014
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Dense semi-rigid scene flow estimation from RGB-D images
J. Quiroga, T. Brox, F. Devernay, and J. L. Crowley · 2014
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Grid-based mapping and tracking in dynamic environments using a uniform evidential environment representation
G. Tanzmeister, J. Thomas, D. Wollherr, and M. Buss · 2014
Stereo matching by training a convolutional neural network to compare image patches
J. Žbontar and Y. LeCun · 2016
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Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios?
A. Behl, O. H. Jafari, S. K. Mustikovela, H. A. Alhaija, C. Rother, and A. Geiger · 2017
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Multi-view 3d object detection network for autonomous driving
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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End-to-end learning of geometry and context for deep stereo regression
A. Kendall, H. Martirosyan, S. Dasgupta, and P. Henry · 2017
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Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Haeusser, C. Hazirbas, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
Cited alongside, same era.
Object scene flow for autonomous vehicles
M. Menze and A. Geiger · 2015
Cited alongside, same era.
Joint 3d estimation of vehicles and scene flow
M. Menze, C. Heipke, and A. Geiger · 2015
Cited alongside, same era.
3d scene flow estimation with a piecewise rigid scene model
C. Vogel, K. Schindler, and S. Roth · 2015
Cited alongside, same era.
3d object tracking using RGB and LIDAR data
A. Asvadi, P. Girao, P. Peixoto, and U. Nunes · 2016
Cited alongside, same era.
Motion-based detection and tracking in 3d lidar scans
A. Dewan, T. Caselitz, G. D. Tipaldi, and W. Burgard · 2016
Cited alongside, same era.
Learning deep correspondence through prior and posterior feature constancy
Z. Liang, Y. Feng, Y. Guo, H. Liu, L. Qiao, W. Chen, L. Zhou, and J. Zhang · 2017
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Spp-net: Deep absolute pose regression with synthetic views
P. Purkait, C. Zhao, and C. Zach · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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Pointnet++: Deep hierarchical feature learning on point sets in a metric space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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A learning approach for real-time temporal scene flow estimation from LIDAR data
A. K. Ushani, R. W. Wolcott, J. M. Walls, and R. M. Eustice · 2017
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3dmatch: Learning local geometric descriptors from rgb-d reconstructions
A. Zeng, S. Song, M. Nießner, M. Fisher, J. Xiao, and T. Funkhouser · 2017
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Flex-convolution (million-scale point-cloud learning beyond grid-worlds)
F. Groh, P. Wieschollek, and H. P. A. Lensch · 2018
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Splatnet: Sparse lattice networks for point cloud processing
H. Su, V. Jampani, D. Sun, S. Maji, E. Kalogerakis, M. Yang, and J. Kautz · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz · 2018
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Deep parametric continuous convolutional neural networks
S. Wang, S. Suo, W.-C. Ma, A. Pokrovsky, and R. Urtasun · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Y. Zhou and O. Tuzel · 2018
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