Fetching the paper…
Reading the bibliography…
The traditional homography estimation pipeline consists of four main steps: feature detection, feature matching, outlier removal and transformation estimation.
M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , vol. 24, no. 6, pp. 381–395, 1981
1981
Earlier work this paper cites.
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision . Cambridge University Press, 2000
2000
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision , vol. 60, no. 2, pp. 91–110, 2004
2004
Earlier work this paper cites.
K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool, “A comparison of affine region detectors,” International Journal of Computer Vision , vol. 65, no. 1, pp. 43–72, 2005
2005
Earlier work this paper cites.
H. Bay, T. Tuytelaars, and L. V. Gool, “Surf: Speeded up robust features,” in European Conference on Computer Vision , 2006, pp. 404–417
2006
Earlier work this paper cites.
S. Baker, A. Datta, and T. Kanade, “Parameterizing homographies,” Carnegie Mellon University, Tech. Rep. CMU-RI-TR-06-11, March 2006
2006
Earlier work this paper cites.
G. Klein and D. W. Murray, “Parallel tracking and mapping for small ar workspaces,” in IEEE International Symposium on Mixed and Augmented Reality , 2007, pp. 1–10
2007
Earlier work this paper cites.
F. Zhou, H. B. Duh, and M. Billinghurst, “Trends in augmented reality tracking, interaction and display: A review of ten years of ismar,” in IEEE International Symposium on Mixed and Augmented Reality , 2008, pp. 193–202
2008
Earlier work this paper cites.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” in IEEE International Conference on Computer Vision , 2011, pp. 1–9
2011
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European Conference on Computer Vision , 2014, pp. 740–755
2014
Earlier work this paper cites.
R. Murartal, J. M. M. Montiel, and J. D. Tardos, “Orb-slam: A versatile and accurate monocular slam system,” IEEE Transactions on Robotics , vol. 31, no. 5, pp. 1147–1163, 2015
2015
Earlier work this paper cites.
D. DeTone, T. Malisiewicz, and A. Rabinovich, “Deep image homography estimation,” arXiv , 2016
2016
Earlier work this paper cites.
J. Justin, A. Alexandre, and F.-F. Li, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision , 2016, pp. 694–711
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European Conference on Computer Vision , 2016, pp. 21–37
2016
Cited alongside, same era.
C. Chang, C. Chou, and E. Y. Chang, “Clkn: Cascaded lucas-kanade networks for image alignment,” in IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 3777–3785
2017
Cited alongside, same era.
R. Murartal and J. D. Tardos, “Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cametas,” IEEE Transactions on Robotics , vol. 33, no. 5, pp. 1255–1262, 2017
2017
Cited alongside, same era.
E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, and C. Rother, “Dsac-differentiable ransac for camera localization,” in IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 6684–6692
2017
Cited alongside, same era.
D. Barath, J. Matas, and J. Noskova, “Magsac: marginalizing sample consensus,” in IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 197–10 205
2019
Later among the works it cites.
P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” in IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 4938–4947
2020
Later among the works it cites.
Y. Li, W. Pei, and Z. He, “Srhen: Stepwise-refining homography estimation network via parsing geometric correspondences in deep latent space,” in ACM International Conference on Multimedia , 2020, pp. 3063–3071
2020
Later among the works it cites.
J. Zhang, C. Wang, S. Liu, L. Jia, N. Ye, J. Wang, J. Zhou, and J. Sun, “Content-aware unsupervised deep homography estimation,” in European Conference on Computer Vision , 2020, pp. 653–669
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Japkowicz, F. E. Nowruzi, and R. Laganiere, “Homography estimation from image pairs with hierarchical convolutional networks,” in IEEE International Conference on Computer Vision Workshops , 2017, pp. 913–920
2017
Cited alongside, same era.
V. Balntas, K. Lenc, A. Vedaldi, and K. Mikolajczyk, “Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors,” in IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 5173–5182
2017
Cited alongside, same era.
K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Transactions on Image Processing , vol. 26, no. 7, pp. 3142–3155, 2017
2017
Cited alongside, same era.
D. Detone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self-supervised interest point detection and description,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 224–236
2018
Cited alongside, same era.
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, “Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume,” in IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8934–8943
2018
Cited alongside, same era.
T. Nguyen, S. W. Chen, S. S. Shivakumar, C. J. Taylor, and V. Kumar, “Unsupervised deep homography: A fast and robust homography estimation model,” in IEEE International Conference on Robotics and Automation , 2018, pp. 2346–2353
2018
Cited alongside, same era.
F. Tang, Y. Wu, X. Hou, and H. Ling, “3d mapping and 6d pose computation for real time augmented reality on cylindrical objects,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 9, pp. 2887–2899, 2019
2019
Cited alongside, same era.
B. Chung and C. Yim, “Bi-sequential video error concealment method using adaptive homography-based registration,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 30, no. 6, pp. 1535–1549, 2019
2019
Cited alongside, same era.
H. Le, F. Liu, S. Zhang, and A. Agarwala, “Deep homography estimation for dynamic scenes,” in IEEE Conference on Computer Vision and Pattern Recognition , 2020, pp. 7652–7661
2020
Later among the works it cites.
L. Nie, C. Lin, K. Liao, S. Liu, and Y. Zhao, “Depth-aware multi-grid deep homography estimation with contextual correlation,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Later among the works it cites.
W. Xue, W. Xie, Y. Zhang, and S. Chen, “Stable linear structures and seam measurements for parallax image stitching,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 1, pp. 253–261, 2021
2021
Later among the works it cites.
X. Shao, L. Zhang, T. Zhang, Y. Shen, and Y. Zhou, “Mofisslam: A multi-object semantic slam system with front-view, inertial and surround-view sensors for indoor parking,” IEEE Transactions on Circuits and Systems for Video Technology , 2021
2021
Later among the works it cites.
X. Zhao, J. Liu, X. Wu, W. Chen, F. Guo, and Z. Li, “Probabilistic spatial distribution prior based attentional keypoints matching network,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 3, pp. 1313–1327, 2021
2021
Later among the works it cites.
D. Koguciuk, E. Arani, and B. Zonooz, “Perceptual loss for robust unsupervised homography estimation,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2021, pp. 4274–4283
2021
Later among the works it cites.
R. Shao, G. Wu, Y. Zhou, Y. Fu, L. Fang, and Y. Liu, “Localtrans: A multiscale local transformer network for cross-resolution homography estimation,” arXiv , 2021
2021
Later among the works it cites.
N. Ye, C. Wang, H. Fan, and S. Liu, “Motion basis learning for unsupervised deep homography estimation with subspace projection,” arXiv , 2021
2021
Later among the works it cites.
J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, “Image matching from handcrafted to deep features: A survey,” International Journal of Computer Vision , vol. 129, no. 1, pp. 23–79, 2021
2021
Later among the works it cites.