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Event cameras provide high temporal precision, low data rates, and high dynamic range visual perception, which are well-suited for optical flow estimation.
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Cited alongside, same era.
D. Falanga, K. Kleber, and D. Scaramuzza, “Dynamic Obstacle Avoidance for Quadrotors with Event Cameras,” Science Robotics , vol. 5, no. 40, p. eaaz9712, 2020
2020
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2020
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2020
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2020
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2020
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2020
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2021
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M. Gehrig, W. Aarents, D. Gehrig, and D. Scaramuzza, “DSEC: A Stereo Event Camera Dataset for Driving Scenarios,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4947–4954, 2021
2021
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Later among the works it cites.
Z. Huang, X. Shi, C. Zhang, Q. Wang, K. C. Cheung, H. Qin, J. Dai, and H. Li, “Flowformer: A Transformer Architecture for Optical Flow,” in Proceedings of the European Conference on Computer Vision . Springer, 2022, pp. 668–685
2022
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Y. Li, X. Liu, W. Dong, H. Zhou, H. Bao, G. Zhang, Y. Zhang, and Z. Cui, “DELTAR: Depth Estimation from a Light-Weight ToF Sensor and RGB Image,” in Proceedings of the European Conference on Computer Vision . Springer, 2022, pp. 619–636
2022
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S. Lin, Y. Ma, Z. Guo, and B. Wen, “DVS-Voltmeter: Stochastic Process-Based Event Simulator for Dynamic Vision Sensors,” in Proceedings of the European Conference on Computer Vision . Springer, 2022, pp. 578–593
2022
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S. Shiba, Y. Aoki, and G. Gallego, “Secrets of Event-based Optical Flow,” in Proceedings of the European Conference on Computer Vision . Springer, 2022, pp. 628–645
2022
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2023
Closest in time.
J. Cuadrado, U. Rançon, B. Cottereau, F. Barranco, and T. Masquelier, “Optical Flow Estimation with Event-based Cameras and Spiking Neural Networks,” Frontiers in Neuroscience , 2023
2023
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M. Denninger, D. Winkelbauer, M. Sundermeyer, W. Boerdijk, M. Knauer, K. H. Strobl, M. Humt, and R. Triebel, “BlenderProc2: A Procedural Pipeline for Photorealistic Rendering,” Journal of Open Source Software , vol. 8, no. 82, p. 4901, 2023. [Online]. Available: https://doi.org/10.21105/joss.04901
2023
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J. Ni, Y. Li, Z. Huang, H. Li, H. Bao, Z. Cui, and G. Zhang, “PATS: Patch Area Transportation with Subdivision for Local Feature Matching,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 776–17 786
2023
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2023
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X. Shi, Z. Huang, D. Li, M. Zhang, K. C. Cheung, S. See, H. Qin, J. Dai, and H. Li, “Flowformer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1599–1610
2023
Closest in time.