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Camera calibration involves estimating camera parameters to infer geometric features from captured sequences, which is crucial for computer vision and robotics.
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Q. Jia, Z. Zhao, X. Feng, J. Liu, Y. Liu, and X. Xue, “Joint edge detection learning for recurrent homography estimation,” in IEEE International Conference on Multimedia and Expo , 2024, pp. 1–6
2024
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2024
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J. Xiao, N. Zhang, D. Tortei, and G. Loianno, “Sthn: Deep homography estimation for uav thermal geo-localization with satellite imagery,” IEEE Robotics and Automation Letters , vol. 9, no. 10, pp. 8754–8761, 2024
2024
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R. Zhang, J. Ma, S.-Y. Cao, L. Luo, B. Yu, S.-J. Chen, J. Li, and H.-L. Shen, “Scpnet: Unsupervised cross-modal homography estimation via intra-modal self-supervised learning,” in European Conference on Computer Vision , 2024, pp. 460–477
2024
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H. Zhu, S.-Y. Cao, J. Hu, S. Zuo, B. Yu, J. Ying, J. Li, and H.-L. Shen, “Mcnet: Rethinking the core ingredients for accurate and efficient homography estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 25 932–25 941
2024
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Y. Liu, H. Li, S. Liu, and B. Zeng, “Codinghomo: Bootstrapping deep homography with video coding,” IEEE Transactions on Circuits and Systems for Video Technology , 2024
2024
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Q. Herau, N. Piasco, M. Bennehar, L. Roldao, D. Tsishkou, C. Migniot, P. Vasseur, and C. Demonceaux, “Soac: Spatio-temporal overlap-aware multi-sensor calibration using neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 131–15 140
2024
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Z. Yang, G. Chen, H. Zhang, K. Ta, I. A. Bârsan, D. Murphy, S. Manivasagam, and R. Urtasun, “Unical: Unified neural sensor calibration,” in European Conference on Computer Vision , 2024, pp. 327–345
2024
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F. Liu, Y. Cao, X. Cheng, and X. Wu, “Transformer-based local-to-global lidar-camera targetless calibration with multiple constraints,” IEEE Transactions on Instrumentation and Measurement , 2024
2024
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Y. Xiao, Y. Li, C. Meng, X. Li, J. Ji, and Y. Zhang, “Calibformer: A transformer-based automatic lidar-camera calibration network,” in IEEE International Conference on Robotics and Automation , 2024, pp. 16 714–16 720
2024
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Y.-C. Lee and K.-W. Chen, “Lccraft: Lidar and camera calibration using recurrent all-pairs field transforms without precise initial guess,” in IEEE International Conference on Robotics and Automation , 2024, pp. 16 669–16 675
2024
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Z. Lin, Z. Gao, X. Liu, J. Wang, W. Song, B. M. Chen, C. Li, Y. Huang, and Y. Zhu, “Sgcalib: A two-stage camera-lidar calibration method using semantic information and geometric features,” in IEEE International Conference on Robotics and Automation , 2024, pp. 14 527–14 533
2024
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A. Zhu, Y. Xiao, C. Liu, M. Tan, and Z. Cao, “Lightweight lidar-camera alignment with homogeneous local-global aware representation,” IEEE Transactions on Intelligent Transportation Systems , 2024
2024
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Z. Luo, G. Yan, X. Cai, and B. Shi, “Zero-training lidar-camera extrinsic calibration method using segment anything model,” in IEEE International Conference on Robotics and Automation , 2024, pp. 14 472–14 478
2024
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X. Hu, Z. Duan, J. Ding, Z. Zhang, X. Huang, and J. Ma, “Lidar-camera extrinsic calibration with hierachical and iterative feature matching,” in IEEE International Conference on Robotics and Automation , 2024, pp. 16 691–16 697
2024
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G. Yan, Z. Luo, Z. Liu, Y. Li, B. Shi, and K. Zhang, “Sensorx2vehicle: Online sensors-to-vehicle rotation calibration methods in road scenarios,” IEEE Robotics and Automation Letters , 2024
2024
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X. Li, Y. Duan, B. Wang, H. Ren, G. You, Y. Sheng, J. Ji, and Y. Zhang, “Edgecalib: Multi-frame weighted edge features for automatic targetless lidar-camera calibration,” IEEE Robotics and Automation Letters , 2024
2024
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S. Zhu and X. Liu, “Revisit self-supervised depth estimation with local structure-from-motion,” in European Conference on Computer Vision . Springer, 2024, pp. 38–56
2024
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J. Hu, M. Mao, H. Bao, G. Zhang, and Z. Cui, “Cp-slam: Collaborative neural point-based slam system,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li, “Gs-slam: Dense visual slam with 3d gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 595–19 604
2024
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K. Wang, Z. Yan, H. Tian, Z. Zhang, X. Li, J. Li, and J. Yang, “Altnerf: Learning robust neural radiance field via alternating depth-pose optimization,” vol. 38, no. 6, pp. 5508–5516, 2024
2024
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2024
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Z. Li and N. Snavely, “Megadepth: Learning single-view depth prediction from internet photos,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2041–2050
2050
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S. Wang, R. Clark, H. Wen, and N. Trigoni, “Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks,” in IEEE International Conference on Robotics and Automation , 2017, pp. 2043–2050
2050
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