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We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
M. Fischler and R. Bolles · 1981
Earlier work this paper cites.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
D. Scharstein and R. Szeliski · 2002
Earlier work this paper cites.
Multiscale structural similarity for image quality assessment
Z. Wang, E. P. Simoncelli, and A. C. Bovik · 2003
Earlier work this paper cites.
Make3D: Depth perception from a single still image
A. Saxena, M. Sun, and A. Y. Ng · 2008
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
Earlier work this paper cites.
Indoor segmentation and support inference from RGBD images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
Earlier work this paper cites.
Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Earlier work this paper cites.
Sun3d: A database of big spaces reconstructed using sfm and object labels
J. Xiao, A. Owens, and A. Torralba · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Microsoft COCO: Common objects in context
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Earlier work this paper cites.
The Cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Deep stereo: Learning to predict new views from the world’s imagery
J. Flynn, I. Neulander, J. Philbin, and N. Snavely · 2016
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Unsupervised CNN for single view depth estimation: Geometry to the rescue
R. Garg, V. K. B.G., G. Carneiro, and I. Reid · 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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Learning depth from single monocular images using deep convolutional neural fields
F. Liu, C. Shen, G. Lin, and I. Reid · 2016
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Pose estimation for augmented reality: A hands-on survey
Tanks and temples: Benchmarking large-scale scene reconstruction
A. Knapitsch, J. Park, Q.-Y. Zhou, and V. Koltun · 2017
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A multi-view stereo benchmark with high-resolution images and multi-camera videos
T. Schops, J. L. Schonberger, S. Galliani, T. Sattler, K. Schindler, M. Pollefeys, and A. Geiger · 2017
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A dataset for benchmarking image-based localization
X. Sun, Y. Xie, P. Luo, and L. Wang · 2017
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High quality monocular depth estimation via transfer learning
I. Alhashim and P. Wonka · 2018
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High quality monocular depth estimation via transfer learning
I. Alhashim and P. Wonka · 2018
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E. Marchand, H. Uchiyama, and F. Spindler · 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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The SYNTHIA dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Monocular depth estimation using neural regression forest
A. Roy and S. Todorovic · 2016
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Matterport3D: Learning from RGB-D data in indoor environments
A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Nießner, M. Savva, S. Song, A. Zeng, and Y. Zhang · 2017
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ScanNet: Richly-annotated 3D reconstructions of indoor scenes
A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Deep ordinal regression network for monocular depth estimation
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Z. Li and N. Snavely · 2018
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On the importance of stereo for accurate depth estimation: An efficient semi-supervised deep neural network approach
N. Smolyanskiy, A. Kamenev, and S. Birchfield · 2018
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FARO™ S350 scanner · 2019
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Packnet-sfm: 3d packing for self-supervised monocular depth estimation
V. Guizilini, R. Ambrus, S. Pillai, and A. Gaidon · 2019
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
K. Lasinger, R. Ranftl, K. Schindler, and V. Koltun · 2019
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