[1] Xu C, Zhou Y J, Luo C. Visual SLAM method based on optical flow and instance segmentation for dynamic scenes[J]. Acta Optica Sinica, 42, 1415002(2022).

[2] Qian Q, Bai T M, Bi Y F et al. Monocular simultaneous localization and mapping initialization method based on point and line features[J]. Acta Optica Sinica, 41, 1215002(2021).

[3] Guo Q D, Quan Y M. Depth image point cloud segmentation using spatial projection[J]. Acta Optica Sinica, 40, 1815001(2020).

[4] Gehrig D, Rebecq H, Gallego G et al. Asynchronous, photometric feature tracking using events and frames[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11216, 766-781(2018).

[5] Amir A, Taba B, Berg D et al. A low power, fully event-based gesture recognition system[C], 7388-7397(2017).

[6] Chen Q, Zhou H Y, Yu F H. Microscopic image mosaic algorithm using improved optical flow method[J]. Laser & Optoelectronics Progress, 58, 2410001(2021).

[7] Lichtsteiner P, Posch C, Delbruck T. A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor[J]. IEEE Journal of Solid-State Circuits, 43, 566-576(2008).

[8] Brandli C, Berner R, Yang M H et al. A 240 × 180 130 dB 3 µs latency global shutter spatiotemporal vision sensor[J]. IEEE Journal of Solid-State Circuits, 49, 2333-2341(2014).

[9] Lucas B D, Kanade T. An iterative image registration technique with an application to stereo vision[C], 674-679(1981).

[10] Horn B K P, Schunck B G. Determining optical flow[J]. Artificial Intelligence, 17, 185-203(1981).

[11] Yu J J, Harley A W, Derpanis K G. Back to basics: unsupervised learning of optical flow via brightness constancy and motion smoothness[M]. Hua G, Jégou H. Computer vision-ECCV 2016 workshops. Lecture notes in computer science, 9915, 3-10(2016).

[12] Yin Z C, Shi J P. GeoNet: unsupervised learning of dense depth, optical flow and camera pose[C], 1983-1992(2018).

[13] Gallego G, Rebecq H, Scaramuzza D. A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation[C], 3867-3876(2018).

[14] Stoffregen T, Kleeman L. Event cameras, contrast maximization and reward functions: an analysis[C], 12292-12300(2019).

[15] Liu D Q, Parra Á, Chin T J. Globally optimal contrast maximisation for event-based motion estimation[C], 6348-6357(2020).

[16] Zhu A Z, Yuan L Z, Chaney K et al. EV-FlowNet: self-supervised optical flow estimation for event-based cameras[C], 26-30.

[17] Zhu A Z, Yuan L Z, Chaney K et al. Live demonstration: unsupervised event-based learning of optical flow, depth and egomotion[C], 1694(2019).

[18] Paredes-Vallés F, Scheper K Y W, de Croon G C H E. Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: from events to global motion perception[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2051-2064(2020).

[19] Chaney K, Panagopoulou A, Lee C et al. Self-supervised optical flow with spiking neural networks and event based cameras[C], 5892-5899(2021).

[20] Benosman R, Clercq C, Lagorce X et al. Event-based visual flow[J]. IEEE Transactions on Neural Networks and Learning Systems, 25, 407-417(2014).

[21] Brosch T, Tschechne S, Neumann H. On event-based optical flow detection[J]. Frontiers in Neuroscience, 9, 137(2015).

[22] Aung M T, Teo R, Orchard G. Event-based plane-fitting optical flow for dynamic vision sensors in FPGA[C](2018).

[23] Czech D, Orchard G. Evaluating noise filtering for event-based asynchronous change detection image sensors[C], 19-24(2016).