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We propose a learning-based method that solves monocular stereo and can be extended to fuse depth information from multiple target frames.
In defense of the eight-point algorithm
R. I. Hartley · 1997
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Multiple View Geometry in Computer Vision
R. Hartley and A. Zisserman · 2004
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Numerical Optimization
J. Nocedal and S. J. Wright · 2006
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Stereo processing by semiglobal matching and mutual information
H. Hirschmuller · 2008
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The BerHu penalty and the grouped effect
L. Zwald and S. Lambert-Lacroix · 2012
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A benchmark for the evaluation of RGB-D SLAM systems
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers · 2012
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SUN3D: A database of big spaces reconstructed using sfm and object labels
J. Xiao, A. Owens, and A. Torralba · 2013
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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MVE – a multi-view reconstruction environment
S. Fuhrmann, F. Langguth, and M. Goesele · 2014
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, and V. Golkov · 2015
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Global, dense multiscale reconstruction for a billion points
B. Ummenhofer and T. Brox · 2015
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ADAM: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
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ADAM: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
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Structure-from-motion revisited
J. L. Schonberger and J.-M. Frahm · 2016
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Pixelwise view selection for unstructured multi-view stereo
J. Schonberger, E. Zheng, M. Pollefeys, and J.-M. Frahm · 2016
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Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
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DeMoN: Depth and motion network for learning monocular stereo
B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox · 2017
Cited alongside, same era.
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
Cited alongside, same era.
MVDepthNet: real-time multiview depth estimation neural network
K. Wang and S. Shen · 2018
Later among the works it cites.
MVSNet: Depth inference for unstructured multi-view stereo
Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan · 2018
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DeepTAM: Deep tracking and mapping
H. Zhou, B. Ummenhofer, and T. Brox · 2018
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PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume
D. Sun, X. Yang, M. Liu, and J. Kautz · 2018
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DeepMVS: Learning multi-view stereopsis
P. Huang, K. Matzen, J. Kopf, N. Ahuja, and J. Huang · 2018
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Learning to solve nonlinear least squares for monocular stereo
R. Clark, M. Bloesch, J. Czarnowski, S. Leutenegger, and A. J. Davison · 2018
Later among the works it cites.
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Optical flow estimation using a spatial pyramid network
A. Ranjan and M. J. Black · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
DeMoN: Depth and motion network for learning monocular stereo
B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox · 2017
Cited alongside, same era.
Learning to solve nonlinear least squares for monocular stereo
R. Clark, M. Bloesch, J. Czarnowski, S. Leutenegger, and A. J. Davison · 2018
Cited alongside, same era.
CodeSLAM — learning a compact, optimisable representation for dense visual SLAM
M. Bloesch, J. Czarnowski, R. Clark, S. Leutenegger, and A. J. Davison · 2018
Cited alongside, same era.
DeepMVS: Learning multi-view stereopsis
P. Huang, K. Matzen, J. Kopf, N. Ahuja, and J. Huang · 2018
Cited alongside, same era.
CodeSLAM — learning a compact, optimisable representation for dense visual SLAM
M. Bloesch, J. Czarnowski, R. Clark, S. Leutenegger, and A. J. Davison · 2018
Later among the works it cites.
BA-Net: Dense bundle adjustment network
C. Tang and P. Tan · 2019
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DPSNet: End-to-end deep plane sweep stereo
S. Im, H. Jeon, S. Lin, and I. S. Kweon · 2019
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Recurrent MVSNet for high-resolution multi-view stereo depth inference
Y. Yao, Z. Luo, S. Li, T. Shen, T. Fang, and L. Quan · 2019
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Learning the depths of moving people by watching frozen people
Z. Li, T. Dekel, F. Cole, R. Tucker, N. Snavely, C. Liu, and W. Freeman · 2019
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