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Image-to-point cloud registration aims to determine the relative camera pose between an RGB image and a reference point cloud, serving as a general solution for locating 3D objects from 2D observations.
R. Sinkhorn and P. Knopp, “Concerning nonnegative matrices and doubly stochastic matrices,” Pacific Journal of Mathematics , vol. 21, no. 2, pp. 343–348, 1967
1967
Earlier work this paper cites.
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.
P. Besl and N. D. McKay, “A method for registration of 3-d shapes,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 14, no. 2, pp. 239–256, 1992
1992
Earlier work this paper cites.
B. S. Reddy and B. N. Chatterji, “An fft-based technique for translation, rotation, and scale-invariant image registration,” IEEE Transactions on Image Processing , vol. 5, no. 8, pp. 1266–1271, 1996
1996
Earlier work this paper cites.
D. G. Lowe, “Object recognition from local scale-invariant features,” in Proceedings of the Seventh IEEE International Conference on Computer Vision , vol. 2. IEEE, 1999, pp. 1150–1157
1999
Earlier work this paper cites.
S. Rusinkiewicz and M. Levoy, “Efficient variants of the icp algorithm,” in Proceedings Third International Conference on 3-D Digital Imaging and Modeling , 2001, pp. 145–152
2001
Earlier work this paper cites.
X. S. Gao, X. R. Hou, J. Tang, and H. F. Cheng, “Complete solution classification for the perspective-three-point problem,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 25, no. 8, pp. 930–943, 2003
2003
Earlier work this paper cites.
D. D. Diel, P. DeBitetto, and S. Teller, “Epipolar constraints for vision-aided inertial navigation,” in 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION’05)-Volume 1 , vol. 2. IEEE, 2005, pp. 221–228
2005
Earlier work this paper cites.
H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part i,” IEEE Robotics and Automation Magazine , vol. 13, no. 2, pp. 99–110, 2006
2006
Earlier work this paper cites.
V. Lepetit, F. Moreno-Noguer, and P. Fua, “Epnp: An accurate o(n) solution to the pnp problem,” International Journal of Computer Vision , vol. 81, pp. 155–166, 2009
2009
Earlier work this paper cites.
Y. Zhong, “Intrinsic shape signatures: A shape descriptor for 3d object recognition,” in 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV workshops . IEEE, 2009, pp. 689–696
2009
Earlier work this paper cites.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” in 2011 International Conference on Computer Vision . IEEE, 2011, pp. 2564–2571
2011
Earlier work this paper cites.
Y. Zheng, Y. Kuang, S. Sugimoto, K. Astrom, and M. Okutomi, “Revisiting the pnp problem: A fast, general and optimal solution,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 2344–2351
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
J. Yang, H. Li, and Y. Jia, “Go-icp: Solving 3d registration efficiently and globally optimally,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 1457–1464
2013
Earlier work this paper cites.
M. Cuturi, “Sinkhorn distances: Lightspeed computation of optimal transport,” Advances in neural information processing systems , vol. 26, 2013
2013
Earlier work this paper cites.
R. Mur-Artal, 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.
E. Hoffer and N. Ailon, “Deep metric learning using triplet network,” in Similarity-Based Pattern Recognition: Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings 3 . Springer, 2015, pp. 84–92
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 652–660
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Cited alongside, same era.
D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self-supervised interest point detection and description,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2018, pp. 224–236
2018
Cited alongside, same era.
J. Li, B. M. Chen, and G. H. Lee, “So-net: Self-organizing network for point cloud analysis,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 9397–9406
2018
Cited alongside, same era.
M. Feng, S. Hu, M. H. Ang, and G. H. Lee, “2d3d-matchnet: Learning to match keypoints across 2d image and 3d point cloud,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 4790–4796
2019
Cited alongside, same era.
F. Lu, G. Chen, Y. Liu, L. Zhang, S. Qu, S. Liu, and R. Gu, “Hregnet: A hierarchical network for large-scale outdoor lidar point cloud registration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 014–16 023
2021
Later among the works it cites.
J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,” IJCV , vol. 129, pp. 1789–1819, 2021
2021
Later among the works it cites.
H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 16 259–16 268
2021
Later among the works it cites.
A.-D. Nguyen, S. Choi, W. Kim, J. Kim, H. Oh, J. Kang, and S. Lee, “Single-image 3-d reconstruction: Rethinking point cloud deformation,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–15, 2022
2022
Later among the works it cites.
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Y. Wang and J. M. Solomon, “Deep closest point: Learning representations for point cloud registration,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 3523–3532
2019
Cited alongside, same era.
Y. Aoki, H. Goforth, R. A. Srivatsan, and S. Lucey, “Pointnetlk: Robust & efficient point cloud registration using pointnet,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7163–7172
2019
Cited alongside, same era.
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6411–6420
2019
Cited alongside, same era.
X. Xu, L. He, H. Lu, L. Gao, and Y. Ji, “Deep adversarial metric learning for cross-modal retrieval,” World Wide Web , vol. 22, pp. 657–672, 2019
2019
Cited alongside, same era.
P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 4938–4947
2020
Cited alongside, same era.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 621–11 631
2020
Cited alongside, same era.
M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C. Hoi, “Deep learning for person re-identification: A survey and outlook,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 6, pp. 2872–2893, 2021
2021
Cited alongside, same era.
J. Li and G. H. Lee, “Deepi2p: Image-to-point cloud registration via deep classification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 960–15 969
2021
Cited alongside, same era.
A. Tsaregorodtsev, J. Muller, J. Strohbeck, M. Herrmann, M. Buchholz, and V. Belagiannis, “Extrinsic camera calibration with semantic segmentation,” in 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2022, pp. 3781–3787
2022
Later among the works it cites.
T. C. Mok and A. Chung, “Affine medical image registration with coarse-to-fine vision transformer,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 20 835–20 844
2022
Later among the works it cites.
Z. Qin, H. Yu, C. Wang, Y. Guo, Y. Peng, and K. Xu, “Geometric transformer for fast and robust point cloud registration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 11 143–11 152
2022
Later among the works it cites.
G. Xu, J. Cheng, P. Guo, and X. Yang, “Attention concatenation volume for accurate and efficient stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 12 981–12 990
2022
Later among the works it cites.
H. Pan, Y. Chen, Z. He, F. Meng, and N. Fan, “Tcdesc: Learning topology consistent descriptors for image matching,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 5, pp. 2845–2855, 2022
2022
Later among the works it cites.
Z. Zhang, Y. Dai, B. Fan, J. Sun, and M. He, “Learning a task-specific descriptor for robust matching of 3d point clouds,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 32, no. 12, pp. 8462–8475, 2022
2022
Later among the works it cites.
Y. Jeon and S.-W. Seo, “EFGHNet: A versatile image-to-point cloud registration network for extreme outdoor environment,” IEEE Robotics and Automation Letters , vol. 7, no. 3, pp. 7511–7517, 2022
2022
Later among the works it cites.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , vol. 42, no. 4, 2023
2023
Closest in time.
Y. Liao, J. Li, S. Kang, Q. Li, G. Zhu, S. Yuan, Z. Dong, and B. Yang, “Se-calib: Semantic edges based lidar-camera boresight online calibration in urban scenes,” IEEE Transactions on Geoscience and Remote Sensing , 2023
2023
Closest in time.
S. Ren, Y. Zeng, J. Hou, and X. Chen, “Corri2p: Deep image-to-point cloud registration via dense correspondence,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 3, pp. 1198–1208, 2023
2023
Closest in time.
L. Li, Y. Ma, K. Tang, X. Zhao, C. Chen, J. Huang, J. Mei, and Y. Liu, “Geo-localization with transformer-based 2d-3d match network,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 4855–4862, 2023
2023
Closest in time.
M. Li, Z. Qin, Z. Gao, R. Yi, C. Zhu, Y. Guo, and K. Xu, “2d3d-matr: 2d-3d matching transformer for detection-free registration between images and point clouds,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 14 128–14 138
2023
Closest in time.
Y. Rao, Y. Ju, C. Li, E. Rigall, J. Yang, H. Fan, and J. Dong, “Learning general descriptors for image matching with regression feedback,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 11, pp. 6693–6707, 2023
2023
Closest in time.
Y. Fu, P. Zhang, B. Liu, Z. Rong, and Y. Wu, “Learning to reduce scale differences for large-scale invariant image matching,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 3, pp. 1335–1348, 2023
2023
Closest in time.
S. Ao, Q. Hu, H. Wang, K. Xu, and Y. Guo, “Buffer: Balancing accuracy, efficiency, and generalizability in point cloud registration,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1255–1264
2023
Closest in time.
Y. Wu, X. Hu, Y. Zhang, M. Gong, W. Ma, and Q. Miao, “Sacf-net: Skip-attention based correspondence filtering network for point cloud registration,” IEEE Transactions on Circuits and Systems for Video Technology , vol. 33, no. 8, pp. 3585–3595, 2023
2023
Closest in time.