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Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion.
Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
H. Zhan, R. Garg, C. Saroj Weerasekera, K. Li, H. Agarwal, and I. Reid · 1912
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Object recognition from local scale-invariant features
D. G. Lowe · 1999
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A flexible new technique for camera calibration
Z. Zhang · 2000
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Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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Surf: Speeded up robust features
H. Bay, T. Tuytelaars, and L. Van Gool · 2006
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Epnp: An accurate o (n) solution to the pnp problem
V. Lepetit, F. Moreno-Noguer, and P. Fua · 2009
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. jia Li, K. Li, and L. Fei-fei · 2009
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Bundle adjustment in the large
S. Agarwal, N. Snavely, S. M. Seitz, and R. Szeliski · 2010
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Orb: An efficient alternative to sift or surf
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski · 2011
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Brisk: Binary robust invariant scalable keypoints
S. Leutenegger, M. Chli, and R. Siegwart · 2011
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Vision meets robotics: The KITTI dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 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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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Lsd-slam: Large-scale direct monocular slam
J. Engel, T. Schöps, and D. Cremers · 2014
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Lift: Learned invariant feature transform
K. M. Yi, E. Trulls, V. Lepetit, and P. Fua · 2016
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Direct sparse odometry
J. Engel, V. Koltun, and D. Cremers · 2017
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Unsupervised monocular depth estimation with left-right consistency
C. Godard, O. Mac Aodha, and G. J. Brostow · 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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evo: Python package for the evaluation of odometry and slam
M. Grupp · 2017
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Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors
V. Balntas, K. Lenc, A. Vedaldi, and K. Mikolajczyk · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras
R. Mur-Artal and J. D. Tardós · 2017
Superdepth: Self-supervised, super-resolved monocular depth estimation
S. Pillai, R. Ambruş, and A. Gaidon · 2019
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Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos
V. Casser, S. Pirk, R. Mahjourian, and A. Angelova · 2019
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Unsupervised scale-consistent depth and ego-motion learning from monocular video
J. Bian, Z. Li, N. Wang, H. Zhan, C. Shen, M.-M. Cheng, and I. Reid · 2019
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Two stream networks for self-supervised ego-motion estimation
R. Ambrus, V. Guizilini, J. Li, S. Pillai, and A. Gaidon · 2019
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Every pixel counts++: Joint learning of geometry and motion with 3d holistic understanding
C. Luo, Z. Yang, P. Wang, Y. Wang, W. Xu, R. Nevatia, and A. Yuille · 2019
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Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
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Sparsity invariant cnns
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger · 2017
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Densepose: Dense human pose estimation in the wild
R. Alp Güler, N. Neverova, and I. Kokkinos · 2018
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Superpoint: Self-supervised interest point detection and description
D. DeTone, T. Malisiewicz, and A. Rabinovich · 2018
Cited alongside, same era.
Lf-net: learning local features from images
Y. Ono, E. Trulls, P. Fua, and K. M. Yi · 2018
Cited alongside, same era.
Discovery of latent 3d keypoints via end-to-end geometric reasoning
S. Suwajanakorn, N. Snavely, J. J. Tompson, and M. Norouzi · 2018
Cited alongside, same era.
Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry
N. Yang, R. Wang, J. Stuckler, and D. Cremers · 2018
Cited alongside, same era.
A. Gordon, H. Li, R. Jonschkowski, and A. Angelova · 2019
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Sganvo: Unsupervised deep visual odometry and depth estimation with stacked generative adversarial networks
T. Feng and D. Gu · 2019
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Deep closest point: Learning representations for point cloud registration
Y. Wang and J. M. Solomon · 2019
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Digging into self-supervised monocular depth estimation
C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow · 2019
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Reinforced feature points: Optimizing feature detection and description for a high-level task
A. Bhowmik, S. Gumhold, C. Rother, and E. Brachmann · 2020
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Superglue: Learning feature matching with graph neural networks
P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich · 2020
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D3feat: Joint learning of dense detection and description of 3d local features
X. Bai, Z. Luo, L. Zhou, H. Fu, L. Quan, and C.-L. Tai · 2020
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Visual odometry revisited: What should be learnt?
H. Zhan, C. S. Weerasekera, J.-W. Bian, and I. Reid · 2020
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Towards better generalization: Joint depth-pose learning without posenet
W. Zhao, S. Liu, Y. Shu, and Y.-J. Liu · 2020
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D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry
N. Yang, L. v. Stumberg, R. Wang, and D. Cremers · 2020
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Deep global registration
C. Choy, W. Dong, and V. Koltun · 2020
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gradslam: Dense slam meets automatic differentiation
J. Krishna Murthy, G. Iyer, and L. Paull · 2020
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Pnp-net: A hybrid perspective-n-point network
R. Sheffer and A. Wiesel · 2020
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3d packing for self-supervised monocular depth estimation
V. Guizilini, R. Ambrus, S. Pillai, A. Raventos, and A. Gaidon · 2020
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