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This paper proposes an end-to-end learning framework for multiview stereopsis.
A theory of shape by space carving
K. N. Kutulakos and S. M. Seitz · 1999
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Photorealistic scene reconstruction by voxel coloring
S. M. Seitz and C. R. Dyer · 1999
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A comparison and evaluation of multi-view stereo reconstruction algorithms
S. M. Seitz, B. Curless, J. Diebel, D. Scharstein, and R. Szeliski · 2006
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A surface-growing approach to multi-view stereo reconstruction
M. Habbecke and L. Kobbelt · 2007
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Real-time visibility-based fusion of depth maps
P. Merrell, A. Akbarzadeh, L. Wang, P. Mordohai, J. Frahm, R. Yang, D. Nistér, and M. Pollefeys · 2007
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Using multiple hypotheses to improve depth-maps for multi-view stereo
N. D. Campbell, G. Vogiatzis, C. Hernández, and R. Cipolla · 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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Continuous depth estimation for multi-view stereo
Y. Liu, X. Cao, Q. Dai, and W. Xu · 2009
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Accurate, dense, and robust multiview stereopsis
Y. Furukawa and J. Ponce · 2010
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Patchmatch stereo-stereo matching with slanted support windows
M. Bleyer, C. Rhemann, and C. Rother · 2011
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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 · 2012
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A novel ray-space based view generation algorithm via radon transform
L. Xu, L. Hou, O. C. Au, W. Sun, X. Zhang, and Y. Guo · 2013
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Self-similarity-based image colorization
J. Pang, O. C. Au, Y. Yamashita, Y. Ling, Y. Guo, and J. Zeng · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Lasagne: First release, Aug. 2015
S. Dieleman, J. Schlüter, C. Raffel, E. Olson, et al · 2015
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Multi-view stereo: A tutorial
Y. Furukawa and C. Hernández · 2015
Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Computing the stereo matching cost with a convolutional neural network
J. Zbontar and Y. LeCun · 2015
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Motion estimation via hierarchical block matching and graph cut
A. Zheng, Y. Yuan, S. P. Jaiswal, and O. C. Au · 2015
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Large-scale data for multiple-view stereopsis
H. Aanæs, R. R. Jensen, G. Vogiatzis, E. Tola, and A. B. Dahl · 2016
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
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Just look at the image: Viewpoint-specific surface normal prediction for improved multi-view reconstruction
S. Galliani and K. Schindler · 2016
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Massively parallel multiview stereopsis by surface normal diffusion
S. Galliani, K. Lasinger, and K. Schindler · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Voxnet: A 3d convolutional neural network for real-time object recognition
D. Maturana and S. Scherer · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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3d shapenets: A deep representation for volumetric shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
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Volumetric and multi-view CNNs for object classification on 3d data
C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas · 2016
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Deep learning 3d shape surfaces using geometry images
A. Sinha, J. Bai, and K. Ramani · 2016
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Deep sliding shapes for amodal 3d object detection in RGB-D images
S. Song and J. Xiao · 2016
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Multi-view 3d models from single images with a convolutional network
M. Tatarchenko, A. Dosovitskiy, and T. Brox · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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