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We present a learning based approach for multi-view stereopsis (MVS).
Handling occlusions in dense multi-view stereo
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M. Goesele, N. Snavely, B. Curless, H. Hoppe, and S. M. Seitz · 2007
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H. Hirschmuller · 2008
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Fast and high quality fusion of depth maps
C. Zach · 2008
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Patchmatch: A randomized correspondence algorithm for structural image editing
C. Barnes, E. Shechtman, A. Finkelstein, and D. B. Goldman · 2009
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Accurate, dense, and robust multiview stereopsis
Y. Furukawa and J. Ponce · 2010
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Multi-view reconstruction preserving weakly-supported surfaces
M. Jancosek and T. Pajdla · 2011
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Efficient large-scale multi-view stereo for ultra high-resolution image sets
E. Tola, C. Strecha, and P. Fua · 2011
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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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Least commitment, viewpoint-based, multi-view stereo
X. Hu and P. Mordohai · 2012
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Accurate multiple view 3d reconstruction using patch-based stereo for large-scale scenes
S. Shen · 2013
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Large scale multi-view stereopsis evaluation
R. Jensen, A. Dahl, G. Vogiatzis, E. Tola, and H. Aanæs · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Patchmatch based joint view selection and depthmap estimation
E. Zheng, E. Dunn, V. Jojic, and J.-M. Frahm · 2014
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Multi-view stereo: A tutorial
Y. Furukawa, C. Hernández, et al · 2015
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Massively parallel multiview stereopsis by surface normal diffusion
S. Galliani, K. Lasinger, and K. Schindler · 2015
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Matchnet: Unifying feature and metric learning for patch-based matching
X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg · 2015
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M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Joint camera clustering and surface segmentation for large-scale multi-view stereo
R. Zhang, S. Li, T. Fang, S. Zhu, and L. Quan · 2015
Learning a multi-view stereo machine
A. Kar, C. Häne, and J. Malik · 2017
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End-to-end learning of geometry and context for deep stereo regression
A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach, and A. Bry · 2017
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Tanks and temples: Benchmarking large-scale scene reconstruction
A. Knapitsch, J. Park, Q.-Y. Zhou, and V. Koltun · 2017
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Semi-supervised deep learning for monocular depth map prediction
Y. Kuznietsov, J. Stückler, and B. Leibe · 2017
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A multi-view stereo benchmark with high-resolution images and multi-camera videos
T. Schöps, J. L. Schönberger, S. Galliani, T. Sattler, K. Schindler, M. Pollefeys, and A. Geiger · 2017
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Cited alongside, same era.
Large-scale data for multiple-view stereopsis
H. Aanæs, R. R. Jensen, G. Vogiatzis, E. Tola, and A. B. Dahl · 2016
Cited alongside, same era.
Unsupervised cnn for single view depth estimation: Geometry to the rescue
R. Garg, G. VijayKumarB., and I. D. Reid · 2016
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Pixelwise view selection for unstructured multi-view stereo
J. L. Schönberger, E. Zheng, J.-M. Frahm, and M. Pollefeys · 2016
Cited alongside, same era.
Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks
J. Xie, R. B. Girshick, and A. Farhadi · 2016
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Stereo matching by training a convolutional neural network to compare image patches
J. Zbontar and Y. LeCun · 2016
Cited alongside, same era.
Unsupervised monocular depth estimation with left-right consistency
C. Godard, O. Mac Aodha, and G. J. Brostow · 2017
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T. Schöps, J. L. Schönberger, S. Galliani, T. Sattler, K. Schindler, M. Pollefeys, and A. Geiger · 2017
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Self-supervised learning for stereo matching with self-improving ability
Y. Zhong, Y. Dai, and H. Li · 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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Deepmvs: Learning multi-view stereopsis
P.-H. Huang, K. Matzen, J. Kopf, N. Ahuja, and J.-B. Huang · 2018
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Supervising the new with the old: Learning SFM from SFM
M. Klodt and A. Vedaldi · 2018
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Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
R. Mahjourian, M. Wicke, and A. Angelova · 2018
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Mvdepthnet: Real-time multiview depth estimation neural network
K. Wang and S. Shen · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan · 2018
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Activestereonet: End-to-end self-supervised learning for active stereo systems
Y. Zhang, S. Khamis, C. Rhemann, J. P. C. Valentin, A. Kowdle, V. Tankovich, M. Schoenberg, S. Izadi, T. A. Funkhouser, and S. R. Fanello · 2018
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