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We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables state-of-the-art non-rigid reconstruction by a learned robust optimization.
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Real-time non-rigid reconstruction using an rgb-d camera
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Flownet: Learning optical flow with convolutional networks
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3d scanning deformable objects with a single rgbd sensor
M. Dou, J. Taylor, H. Fuchs, A. Fitzgibbon, and S. Izadi · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and k. kavukcuoglu · 2015
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Dynamicfusion: Reconstruction and tracking of non-rigid scenes in real-time
R. A. Newcombe, D. Fox, and S. M. Seitz · 2015
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The fast bilateral solver
J. T. Barron and B. Poole · 2016
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Fusion4d: Real-time performance capture of challenging scenes
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Volumedeform: Real-time volumetric non-rigid reconstruction
M. Innmann, M. Zollhöfer, M. Nießner, C. Theobalt, and M. Stamminger · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Clkn: Cascaded lucas-kanade networks for image alignment
C.-H. Chang, C.-N. Chou, and E. Y. Chang · 2017
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Real-time geometry, albedo, and motion reconstruction using a single rgb-d camera
K. Guo, F. Xu, T. Yu, X. Liu, Q. Dai, and Y. Liu · 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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Killingfusion: Non-rigid 3d reconstruction without correspondences
M. Slavcheva, M. Baust, D. Cremers, and S. Ilic · 2017
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Bodyfusion: Real-time capture of human motion and surface geometry using a single depth camera
End-to-end cad model retrieval and 9dof alignment in 3d scans
A. Avetisyan, A. Dai, and M. Nießner · 2019
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Pointflownet: Learning representations for rigid motion estimation from point clouds
A. Behl, D. Paschalidou, S. Donné, and A. Geiger · 2019
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Joint-task self-supervised learning for temporal correspondence
X. Li, S. Liu, S. De Mello, X. Wang, J. Kautz, and M.-H. Yang · 2019
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Taking a deeper look at the inverse compositional algorithm
Z. Lv, F. Dellaert, J. M. Rehg, and A. Geiger · 2019
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Deep rigid instance scene flow
W.-C. Ma, S. Wang, R. Hu, Y. Xiong, and R. Urtasun · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Machine learning-aided numerical linear algebra: Convolutional neural networks for the efficient preconditioner generation
M. Götz and H. Anzt · 2018
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Regnet: Learning the optimization of direct image-to-image pose registration
L. Han, M. Ji, L. Fang, and M. Nießner · 2018
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Sobolevfusion: 3d reconstruction of scenes undergoing free non-rigid motion
M. Slavcheva, M. Baust, and S. Ilic · 2018
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Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume
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Ba-net: Dense bundle adjustment network
C. Tang and P. Tan · 2018
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Doublefusion: Real-time capture of human performances with inner body shapes from a single depth sensor
T. Yu, Z. Zheng, K. Guo, J. Zhao, Q. Dai, H. Li, G. Pons-Moll, and Y. Liu · 2018
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Deep learning of preconditioners for conjugate gradient solvers in urban water related problems
J. Sappl, L. Seiler, M. Harders, and W. Rauch · 2019
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Learning correspondence from the cycle-consistency of time
X. Wang, A. Jabri, and A. A. Efros · 2019
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Deepdeform: Learning non-rigid rgb-d reconstruction with semi-supervised data
A. Božič, M. Zollhöfer, C. Theobalt, and M. Nießner · 2020
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Mast: A memory-augmented self-supervised tracker
Z. Lai, E. Lu, and W. Xie · 2020
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Learning to optimize non-rigid tracking
Y. Li, A. Božič, T. Zhang, Y. Ji, T. Harada, and M. Nießner · 2020
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Flownet3d++: Geometric losses for deep scene flow estimation
Z. Wang, S. Li, H. Howard-Jenkins, V. Prisacariu, and M. Chen · 2020
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