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Received：04 March 2022，
Revised：2022-3-31，
Accepted：07 April 2022，
Published：16 June 2022
移动端阅览
视频是视觉信息处理的基础概念，传统视频的帧率只有几十Hz，不能记录光的高速变化过程，成为限制机器视觉速度的天花板，其根本原因在于视频概念脱胎于胶片成像，未能发挥电子和数字技术的潜力。脉冲视觉模型通过感光器件捕获光子，累积能量达到约定阈值时产生脉冲，形成脉冲的时间越长，表明收到的光信号越弱，反之光信号越强，据此可估计任意时刻的光强，从而实现连续成像。采用普通器件，研制了比影视视频快千倍的超高速成像芯片和相机，进而基于脉冲神经网络实现了超高速目标检测、跟踪和识别，打破了机器视觉提速依赖算力线性增长的传统范式。本文从脉冲视觉模型表达视觉信息的生物学基础和物理原理出发，介绍了脉冲视觉原理的软件模拟器及其模拟真实世界光子传播的计算过程，描述了基于脉冲视觉原理的高灵敏光电传感器件及芯片的工作机理和结构设计、基于脉冲视觉的影像重建原理以及脉冲视觉信号与普通图像信号融合的计算摄像算法与计算摄像系统，介绍了基于脉冲神经网络的超高速运动目标检测、跟踪与识别，通过对比国际国内相关研究内容和发展现状，展望了脉冲视觉的发展与演进方向。脉冲视觉芯片和系统在工业(高铁、电力和轮机等不停机监测，智能制造高速监视等)、民用(高速相机、智能交通、辅助驾驶、司法取证和体育判罚等)以及国防(高速对抗)等领域都具有巨大应用潜力，是未来值得重点关注和研究的一个重要方向。
Video is the conceptual base of visual information processing technology. The frame rate of traditional video is tens of Hertz
which is incapable to represent the Ultra-high speed change process of light. It also constrains to the speed of machine vision. The concept of video is originated from film imaging
which can't unleash the full potential of electronic and digital technology. The spike vision model generates a spike when the photon energy captured by a photosensitive device reaches the predefined threshold. The longer the spike timing is
the weaker the received light signals are. The occurred light intensity can be inferred to achieve consistent imaging. Current research of spiking vision is focused on developing disruptive ultra-high speed imaging chips and cameras that are 1 000 times faster than videos. It is substituting traditional paradigm derived from machine vision speed linear growth of computing power. Based on the biological evidence and physical principle of spiking vision
our review analyzes the software simulator of spiking vision
the computational process of the photon propagation simulation
spiking vision based visual image reconstruction
and the detailed mechanism of photoelectric sensor structure and the chip design. Our review reveals the ultra-high speed target motion detection
and spiking neural networks based tracking and recognition. The evolution of spiking vision is forecasted. Future spiking vision based chip and system has multifaceted potentials to further harness the industrial tasks (e.g.
the monitoring of high-speed train
power grid
turbine and intelligent manufacturing)
civil applications (e.g.
high-speed camera
intelligent transportation
auxiliary driving
judicial forensics and sports arbitration)
and national defense construction (e.g.
high-speed confrontation campaigns).
Adelson E H and Bergen J R. 1991. The plenoptic function and the elements of early vision//Landy M and Movshon J A, eds. Computational Models of Visual Processing. Cambridge: MIT Press: 3-20 [ DOI: 10.7551/mitpress/2002.003.0004 http://dx.doi.org/10.7551/mitpress/2002.003.0004 ]
Andreopoulos A, Kashyap H J, Nayak T K, Amir A and Flickner M D. 2018. A low power, high throughput, fully event-based stereo system//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 7532-7542 [ DOI: 10.1109/CVPR.2018.00786 http://dx.doi.org/10.1109/CVPR.2018.00786 ]
Baldwin R W, Almatrafi M, Asari V and Hirakawa K. 2020. Event probability mask (EPM) and event denoising convolutional neural network (EDnCNN) for neuromorphic cameras//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 1698-1707 [ DOI: 10.1109/CVPR42600.2020.00177 http://dx.doi.org/10.1109/CVPR42600.2020.00177 ]
Bardow P, Davison A J and Leutenegger S. 2016. Simultaneous optical flow and intensity estimation from an event camera//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE: 884-892 [ DOI: 10.1109/CVPR.2016.102 http://dx.doi.org/10.1109/CVPR.2016.102 ]
Barlow H B. 1953. Summation and inhibition in the frog's retina. The Journal of Physiology, 119(1): 69-88 [DOI: 10.1113/jphysiol.1953.sp004829]
Barranco F, Fermuller C and Ros E. 2018. Real-time clustering and multi-target tracking using event-based sensors//Proceedings of 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems. Madrid, Spain: IEEE: 5764-5769 [ DOI: 10.1109/IROS.2018.8593380 http://dx.doi.org/10.1109/IROS.2018.8593380 ]
Belbachir A N, Hofstätter M, Litzenberger M and Schön P. 2011. High-speed embedded-object analysis using a dual-line timed-address-event temporal-contrast vision sensor. IEEE Transactions on Industrial Electronics, 58(3): 770-783 [DOI: 10.1109/TIE.2010.2095390]
Benosman R, Ieng S H, Clercq C, Bartolozzi C and Srinivasan M. 2012. Asynchronous frameless event-based optical flow. Neural Networks, 27: 32-37 [DOI: 10.1016/j.neunet.2011.11.001]
Boahen K A. 2000. Point-to-point connectivity between neuromorphic chips using address events. IEEE Transactions on Circuits and Systems Ⅱ: Analog and Digital Signal Processing, 47(5): 416-434 [DOI: 10.1109/82.842110]
Brandli C, Berner R, Yang M H, Liu S C and Delbruck T. 2014. A 240×180 130 db 3 μs latency global shutter spatiotemporal vision sensor. IEEE Journal of Solid-State Circuits, 49(10): 2333-2341 [DOI: 10.1109/JSSC.2014.2342715]
Chen G, Cao H, Aafaque M, Chen J N, Ye C B, Röhrbein F, Conradt J, Chen K, Bing Z S, Liu X B, Hinz G, Stechele W and Knoll A. 2018. Neuromorphic vision based multivehicle detection and tracking for intelligent transportation system. Journal of Advanced Transportation, 2018: #4815383 [DOI: 10.1155/2018/4815383]
Chen H S, Suter D, Wu Q Q and Wang H Z. 2020. End-to-end learning of object motion estimation from retinal events for event-based object tracking//Proceedings of the 34th AAAI Conference on Artificial Intelligence. New York, USA: AAAI: 10534-10541 [ DOI: 10.1609/aaai.v34i07.6625 http://dx.doi.org/10.1609/aaai.v34i07.6625 ]
Chen H S, Wu Q Q, Liang Y J, Gao X B and Wang H Z. 2019. Asynchronous tracking-by-detection on adaptive time surfaces for event-based object tracking//Proceedings of the 27th ACM International Conference on Multimedia. Nice, France: ACM: 473-481 [ DOI: 10.1145/3343031.3350975 http://dx.doi.org/10.1145/3343031.3350975 ]
Chi Y H, Gnanasambandam A, Koltun V and Chan S H. 2020. Dynamic low-light imaging with quanta image sensors//Proceedings of the 16th European Conference on Computer Vision. Glasgow, UK: Springer: 122-138
Chin T J, Bagchi S, Eriksson A and van Schaik A. 2019. Star tracking using an event camera//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach, USA: IEEE: 1646-1655 [ DOI: 10.1109/CVPRW.2019.00208 http://dx.doi.org/10.1109/CVPRW.2019.00208 ]
Dosovitskiy A, Ros G, Codevilla F, López A and Koltun V. 2017. CARLA: an open urban driving simulator//Proceedings of the 1st Annual Conference on Robot Learning. Mountain View, USA: PMLR: 1-16
Falanga D, Kleber K and Scaramuzza D. 2020. Dynamic obstacle avoidance for quadrotors with event cameras. Science Robotics, 5(40): #eaaz9712 [DOI: 10.1126/scirobotics.aaz9712]
Gallego G, Rebecq H and Scaramuzza D. 2018. A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 3867-3876 [ DOI: 10.1109/CVPR.2018.00407 http://dx.doi.org/10.1109/CVPR.2018.00407 ]
Gehrig M, Millhäusler M, Gehrig D and Scaramuzza D. 2021. E-RAFT: dense optical flow from event cameras [EB/OL ] . [2022-02-15 ] . https://arxiv.org/pdf/2108.10552.pdf https://arxiv.org/pdf/2108.10552.pdf
Guo M H, Ding R X and Chen S S. 2016. Live demonstration: a dynamic vision sensor with direct logarithmic output and full-frame picture-on-demand//2016 IEEE International Symposium on Circuits and System. Montreal, Canada: IEEE: 456 [ DOI: 10.1109/ISCAS.2016.7527274 http://dx.doi.org/10.1109/ISCAS.2016.7527274 ]
Gyongy I, Dutton N A W and Henderson R K. 2018. Single-photon tracking for high-speed vision. Sensors, 18(2): #323 [DOI: 10.3390/s18020323]
Han J, Zhou C, Duan P Q, Tang Y H, Xu C, Xu C, Huang T J and Shi B X. 2020. Neuromorphic camera guided high dynamic range imaging//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 1727-1736 [ DOI: 10.1109/CVPR42600.2020.00180 http://dx.doi.org/10.1109/CVPR42600.2020.00180 ]
Hinz G, Chen G, Aafaque M, Röhrbein F, Conradt J, Bing Z S, Qu Z N, Stechele W and Knoll A. 2017. Online multi-object tracking-by-clustering for intelligent transportation system with neuromorphic vision sensor//Proceedings of the 40th Annual German Conference on AI. Dortmund, Germany: Springer: 142-154 [ DOI: 10.1007/978-3-319-67190-1_11 http://dx.doi.org/10.1007/978-3-319-67190-1_11 ]
Hu L W, Zhao R, Ding Z L, Ma L, Shi B X, Xiong R Q and Huang T J. 2021a. Optical flow estimation for spiking camera [EB/OL ] . [2022-02-15 ] . https://arxiv.org/pdf/2110.03916.pdf https://arxiv.org/pdf/2110.03916.pdf
Hu Y H, Liu S C and Delbruck T. 2021b. V2e: from video frames to realistic DVS events//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Nashville, USA: IEEE: 1312-1321 [ DOI: 10.1109/CVPRW53098.2021.00144 http://dx.doi.org/10.1109/CVPRW53098.2021.00144 ]
Huang J, Wang S Z, Guo M H and Chen S S. 2018. Event-guided structured output tracking of fast-moving objects using a CeleX sensor. IEEE Transactions on Circuits and Systems for Video Technology, 28(9): 2413-2417 [DOI: 10.1109/TCSVT.2018.2841516]
Huang T J, Shi L P, Tang H J, Pan G, Chen Y J and Yu J Q. 2016. Research on multimedia technology 2015—advances and trend of brain-like computing. Journal of Image and Graphics, 21(11): 1411-1424
黄铁军, 施路平, 唐华锦, 潘纲, 陈云霁, 于俊清. 2016. 多媒体技术研究: 2015——类脑计算的研究进展与发展趋势. 中国图象图形学报, 21(11): 1411-1424 [DOI: 10.11834/jig.20161101]
Huang T J, Yu Z F and Liu Y J. 2019. Brain-like machine: thought and architecture. Journal of Computer Research and Development, 56(6): 1135-1148
黄铁军, 余肇飞, 刘怡俊. 2019. 类脑机的思想与体系结构综述. 计算机研究与发展, 56(6): 1135-1148 [DOI: 10.7544/issn1000-1239.2019.20190240]
Hubel D H and Wiesel T N. 1959. Receptive fields of single neurones in the cat's striate cortex. The Journal of Physiology, 148(3): 574-591 [DOI: 10.1113/jphysiol.1959.sp006308]
Ingle A, Seets T, Buttafava M, Gupta S, Tosi A, Gupta M and Velten A. 2021. Passive inter-photon imaging//Proceedingsof 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 8581-8591 [ DOI: 10.1109/CVPR46437.2021.00848 http://dx.doi.org/10.1109/CVPR46437.2021.00848 ]
Jiang H Z, Sun D Q, Jampani V, Yang M H, Learned-Miller E and Kautz J. 2018. Super SloMo: high quality estimation of multiple intermediate frames for video interpolation//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 9000-9008 [ DOI: 10.1109/CVPR.2018.00938 http://dx.doi.org/10.1109/CVPR.2018.00938 ]
Jing Y C, Yang Y D, Wang X C, Song M L and Tao D C. 2021. Turning frequency to resolution: video super-resolution via event cameras//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 7768-7777 [ DOI: 10.1109/CVPR46437.2021.00768 http://dx.doi.org/10.1109/CVPR46437.2021.00768 ]
Kim H, Handa A, Benosman R, Ieng S H and Davison A. 2008. Simultaneous mosaicing and tracking with an event camera//Proceedings British Machine Vision Conference 2014. Nottingham, UK: BMVA Press [ DOI: 10.5244/C.28.26 http://dx.doi.org/10.5244/C.28.26 ]
Kramer J. 2002. An integrated optical transient sensor. IEEE Transactions on Circuits and Systems Ⅱ: Analog and Digital Signal Processing, 49(9): 612-628 [DOI: 10.1109/TCSⅡ.2002.807270]
Lee C, Kosta A K, Zhu A Z, Chaney K, Daniilidis K and Roy K. 2020. Spike-FlowNet: event-based optical flow estimation with energy-efficient hybrid neural networks//Proceedings of the 16th European Conference on Computer Vision. Glasgow, Scotland: Springer: 366-382
Lettvin J Y, Maturana H R, McCulloch W S and Pitts W H. 1959. What the frog's eye tells the frog's brain. Proceedings of the IRE, 47(11): 1940-1951 [DOI: 10.1109/JRPROC.1959.287207]
Li J N, Dong S W, Yu Z F, Tian Y H and Huang T J. 2019. Event-based vision enhanced: a joint detection framework in autonomous driving//Proceedings of 2019 IEEE International Conference on Multimedia and Expo. Shanghai, China: IEEE: 1396-1401 [ DOI: 10.1109/ICME.2019.00242 http://dx.doi.org/10.1109/ICME.2019.00242 ]
Li J N and Tian Y H. 2021. Recent advances in neuromorphic vision sensors: a survey. Chinese Journal of Computers, 44(6): 1258-1286
李家宁, 田永鸿. 2021. 神经形态视觉传感器的研究进展及应用综述. 计算机学报, 44(6): 1258-1286 [DOI: 10.11897/SP.J.1016.2021.01258]
Leñero-Bardallo J A, Carmona-Galán R and Rodríguez-VázquezÁ. 2015. A high dynamic range image sensor with linear response based on asynchronous event detection//Proceedings of European Conference on Circuit Theory and Design. Trondheim, Norway: IEEE: 1-4 [ DOI: 10.1109/ECCTD.2015.7300079 http://dx.doi.org/10.1109/ECCTD.2015.7300079 ]
Leñero-Bardallo J A, Guerrero-Rodriguez J M, Carmona-Galan R and Rodriguez-Vazquez A. 2018. On the analysis and detection of flames with an asynchronous spiking image sensor. IEEE Sensors Journal, 18(16): 6588-6595 [DOI: 10.1109/JSEN.2018.2851063]
Lichtsteiner P, Delbruck T and Kramer J. 2004. Improved ON/OFF temporally differentiating address-event imager//Proceedings of the 11th IEEE International Conference on Electronics, Circuits and Systems. Tel Aviv, Israel: IEEE: 211-214 [ DOI: 10.1109/ICECS.2004.1399652 http://dx.doi.org/10.1109/ICECS.2004.1399652 ]
Ma S Z, Gupta S, Ulku A C, Bruschini C, Charbon E and Gupta M. 2020. Quanta burst photography. ACM Transactions on Graphics, 39(4): #79 [DOI: 10.1145/3386569.3392470]
Mahowald M. 1992. VLSI Analogs of Neuronal Visual Processing: a Synthesis of form and Function. Pasadena: California Institute of Technology [ DOI: 10.7907/4bdw-fg34 http://dx.doi.org/10.7907/4bdw-fg34 ]
Mead C A and Mahowald M A. 1988. A silicon model of early visual processing. Neural Networks, 1(1): 91-97 [DOI: 10.1016/0893-6080(88)90024-X]
Morrical N, Tremblay J, Lin Y, Tyree S, Birchfield S, Pascucci V and Wald I. 2021. Nvisii: a scriptable tool for photorealistic image gener ation [EB/OL ] . [2022-02-15 ] . https://arxiv.org/pdf/2105.13962.pdf https://arxiv.org/pdf/2105.13962.pdf
Mostafavi I S M, Choi J and Yoon K J. 2020. Learning to super resolve intensity images from events//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 2765-2773 [ DOI: 10.1109/CVPR42600.2020.00284 http://dx.doi.org/10.1109/CVPR42600.2020.00284 ]
Ni Z J, Bolopion A, Agnus J, Benosman R and Regnier S. 2012. Asynchronous event-based visual shape tracking for stable haptic feedback in microrobotics. IEEE Transactions on Robotics, 28(5): 1081-1089 [DOI: 10.1109/TRO.2012.2198930]
Orchard G, Benosman R, Etienne-Cummings R and Thakor N V. 2013. A spiking neural network architecture for visual motion estimation//Proceedings of 2013 IEEE Biomedical Circuits and Systems Conference. Rotterdam, the Netherlands: IEEE: 298-301 [ DOI: 10.1109/BioCAS.2013.6679698 http://dx.doi.org/10.1109/BioCAS.2013.6679698 ]
Pan L Y, Scheerlinck C, Yu X, Hartley R, Liu M M and Dai Y C. 2019. Bringing a blurry frame alive at high frame-rate with an event camera//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE: 6813-6822 [ DOI: 10.1109/CVPR.2019.00698 http://dx.doi.org/10.1109/CVPR.2019.00698 ]
Paredes-Valles F, Scheper K Y W and de Croon G C H E. 2020. Unsupervised learning of a hierarchical spiking neural network for optical flow estimation: from events to global motion perception. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(8): 2051-2064 [DOI: 10.1109/TPAMI.2019.2903179]
Posch C, Matolin D and Wohlgenannt R. 2010. A QVGA 143 dB dynamic range frame-free PWM image sensor with lossless pixel-level video compression and time-domain CDS. IEEE Journal of Solid-State Circuits, 46(1), 259-275 [DOI: 10.1109/JSSC.2010.2085952]
Pei J, Deng L, Song S, Zhao M, Zhang Y, Wu S, Wang G, Zou Z, Wu Z, He W and Chen F. 2019. Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature, 572(7767): 106-111 [DOI: 10.1038/s41586-019-1424-8]
Qiu W C, Zhong F W, Zhang Y, Qiao S Y, Xiao Z H, Kim T S and Wang Y Z. 2017. UnrealCV: virtual worlds for computer vision//Proceedings of the 25th ACM international conference on Multimedia. Lisboa, the Portuguese Republic: ACM: 1221-1224 [ DOI: 10.1145/3123266.3129396 http://dx.doi.org/10.1145/3123266.3129396 ]
Rodriguez J A, Mauger J, Parameswaran S, Zeller-Townson R and Cauble G. 2021. Dynamic vision sensor datasets in the maritime domain//Proceedings of SPIE 11870, Artificial Intelligence and Machine Learning in Defense Applications Ⅲ. Spain: SPIE: 118700G [ DOI: 10.1117/12.2600971 http://dx.doi.org/10.1117/12.2600971 ]
Rueckauer B and Delbruck T. 2016. Evaluation of event-based algorithms for optical flow with ground-truth from inertial measurement sensor. Frontiers in Neuroscience, 10: #176 [DOI: 10.3389/fnins.2016.00176]
Sang Y S, Li R H, Li Y Q, Wang Q W and Mao Y. 2019. Research on neuromorphic vision sensor and its applications. Chinese Journal on Internet of Things, 3(4): 63-71
桑永胜, 李仁昊, 李耀仟, 王蔷薇, 毛耀. 2019. 神经形态视觉传感器及其应用研究. 物联网学报, 3(4): 63-71 [DOI: 10.11959/j.issn.2096-3750.2019.00133]
Scheerlinck C, Barnes N and Mahony R. 2019. Continuous-time intensity estimation using event cameras//Proceedings of the 14th Asian Conference on Computer Vision. Perth, Australia: Springer: 308-324 [ DOI: 10.1007/978-3-030-20873-8_20 http://dx.doi.org/10.1007/978-3-030-20873-8_20 ]
Son B, Suh Y, Kim S, Jung H, Kim J S, Shin C, Park K, Lee K, Park J, Woo J and Roh Y. 2017. 4.1 A 640×480 dynamic vision sensor with a 9 μm pixel and 300Meps address-event representation//Proceedings of the 2017 IEEE International Solid-State Circuits Conference. San Francisco, USA: IEEE: 66-67 [ DOI: 10.1109/ISSCC.2017.7870263 http://dx.doi.org/10.1109/ISSCC.2017.7870263 ]
Schraml S and Belbachir A N. 2010. A spatio-temporal clustering method using real-time motion analysis on event-based 3D vision//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. San Francisco, USA: IEEE: 57-63 [ DOI: 10.1109/CVPRW.2010.5543810 http://dx.doi.org/10.1109/CVPRW.2010.5543810 ]
Seets T, Ingle A, Laurenzis M and Velten A. 2021. Motion adaptive deblurring with single-photon cameras//Proceedings of 2021 IEEE Winter Conference on Applications of Computer Vision. Waikoloa, USA: IEEE: 1944-1953 [ DOI: 10.1109/WACV48630.2021.00199 http://dx.doi.org/10.1109/WACV48630.2021.00199 ]
Shah S, Dey D, Lovett C and Kapoor A. 2018. Airsim: high-fidelity visual and physical simulation for autonomous vehicles//Hutter M and Siegwart R, eds. Field and Service Robotics: Results of the 11th International Conference. Cham: Springer: 621-635 [ DOI: 10.1007/978-3-319-67361-5_40 http://dx.doi.org/10.1007/978-3-319-67361-5_40 ]
Sivilotti M A. 1991. Wiring Considerations in Analog VLSI Systems, with Application to Field-Programmable Networks. Pasadena, USA: California Institute of Technology [ DOI: 10.7907/stj4-kh72 http://dx.doi.org/10.7907/stj4-kh72 ]
Stoffregen T, Gallego G, Drummond T, Kleeman L and Scaramuzza D. 2019. Event-based motion segmentation by motion compensation//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea(South): IEEE: 7243-7252 [ DOI: 10.1109/ICCV.2019.00734 http://dx.doi.org/10.1109/ICCV.2019.00734 ]
Stoffregen T, Scheerlinck C, Scaramuzza D, Drummond T, Barnes N, Kleeman L and Mahony R. 2020. Reducing the sim-to-real gap for event cameras//Proceedings of the 16th European Conference on Computer Vision. Glasgow, Scotland: Springer: 534-549 [ DOI: 10.1007/978-3-030-58583-9_32 http://dx.doi.org/10.1007/978-3-030-58583-9_32 ]
Sun D, Yang X, Liu M Y and Kautz J, 2018. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Louisiana, USA: IEEE: 8934-8943
Tulyakov S, Fleuret F, Kiefel M, Gehler P and Hirsch M. 2019. Learning an event sequence embedding for dense event-based deep stereo//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea(South): IEEE: 1527-1537 [ DOI: 10.1109/ICCV.2019.00161 http://dx.doi.org/10.1109/ICCV.2019.00161 ]
Tulyakov S, Gehrig D, Georgoulis S, Erbach J, Gehrig M, Li Y Y and Scaramuzza D. 2021. Time Lens: Event-Based Video Frame Interpolation//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Virtual: IEEE: 16155-16164
Wang C, Chen F, Wen D S, Lei H, Song Z X and Zhao H F. 2021. Review on imaging and data processing of visual sensing. Journal of Image and Graphics, 26(6): 1450-1469
王程, 陈峰, 汶德胜, 雷浩, 宋宗玺, 赵航芳. 2021. 视觉传感成像技术与数据处理进展. 中国图象图形学报, 26(6): 1450-1469 [DOI: 10.11834/jig.200852]
Yang Z Y, Wu Y J, Wang G R, Yang Y K, Li G Q, Deng L, Zhu J and Shi L P. 2019. DashNet: a hybrid artificial and spiking neural network for high-speed object tracking [EB/OL ] . [2022-02-15 ] . https://arxiv.org/pdf/1909.12942.pdf https://arxiv.org/pdf/1909.12942.pdf
Ye C X, Mitrokhin A, Fermüller C, Yorke J A and Aloimonos Y. 2020. Unsupervised learning of dense optical flow, depth and egomotion with event-based sensors//Proceedings of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems. Las Vegas, USA: IEEE: 5831-5838 [ DOI: 10.1109/IROS45743.2020.9341224 http://dx.doi.org/10.1109/IROS45743.2020.9341224 ]
Zhao H, Shi B X, Fernandez-Cull C, Yeung S K and Raskar R. 2015. Unbounded high dynamic range photography using a modulo camera//Proceedings of 2015 IEEE International Conference on Computational Photography. Houston, USA: IEEE: 1-10 [ DOI: 10.1109/ICCPHOT.2015.7168378 http://dx.doi.org/10.1109/ICCPHOT.2015.7168378 ]
Zhao J, Xie J Y, Xiong R Q, Zhang J, Yu Z F and Huang T J. 2021a. Super resolve dynamic scene from continuous spike streams//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal, Canada: IEEE: 2513-2522 [ DOI: 10.1109/ICCV48922.2021.00253 http://dx.doi.org/10.1109/ICCV48922.2021.00253 ]
Zhao J, Xiong R Q and Huang T J. 2020a. High-speed motion scene reconstruction for spike camera via motion aligned filtering//Proceedings of 2020 IEEE International Symposium on Circuits and Systems. Seville, Spain: IEEE: 1-5 [ DOI: 10.1109/ISCAS45731.2020.9181055 http://dx.doi.org/10.1109/ISCAS45731.2020.9181055 ]
Zhao J, Xiong R Q, Liu H F, Zhang J and Huang T J. 2021b. Spk2ImgNet: learning to reconstruct dynamic scene from continuous spike stream//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 11991-12000 [ DOI: 10.1109/CVPR46437.2021.01182 http://dx.doi.org/10.1109/CVPR46437.2021.01182 ]
Zhao J, Xiong R Q, Zhao R, Wang J, Ma S W and Huang T J. 2020b. Motion estimation for spike camera data sequence via spike interval analysis//Proceedings of 2020 IEEE International Conference on Visual Communications and Image Processing. Macau, China: IEEE: 371-374 [ DOI: 10.1109/VCIP49819.2020.9301840 http://dx.doi.org/10.1109/VCIP49819.2020.9301840 ]
Zheng Y J, Zheng L X, Yu Z F, Shi B X, Tian Y H and Huang T J. 2021. High-speed image reconstruction through short-term plasticity for spiking cameras//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 6354-6363 [ DOI: 10.1109/CVPR46437.2021.00629 http://dx.doi.org/10.1109/CVPR46437.2021.00629 ]
Zhou C, Zhao H, Han J, Xu C, Xu C, Huang T J and Shi B X. 2020. UnModNet: learning to unwrap a modulo image for high dynamic range imaging//Proceedings of the 33rd International Conference on Neural Information Processing Systems. Vancouver, Canada: MIT Press: 1559-1570
Zhu A Z, Chen Y B and Daniilidis K. 2018a. Realtime time synchronized event-based stereo//Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer: 438-452 [ DOI: 10.1007/978-3-030-01231-1_27 http://dx.doi.org/10.1007/978-3-030-01231-1_27 ]
Zhu A Z, Thakur D, Özaslan T, Pfrommer B, Kumar V and Daniilidis K. 2018b. The multivehicle stereo event camera dataset: an event camera dataset for 3D perception. IEEE Robotics and Automation Letters, 3(3): 2032-2039 [DOI: 10.1109/LRA.2018.2800793]
Zhu A Z, Wang Z Y, Khant K and Daniilidis K. 2021. EventGAN: leveraging large scale image datasets for event cameras//Proceedings of 2021 IEEE International Conference on Computational Photography. Haifa, Israel: IEEE: 1-11 [ DOI: 10.1109/ICCP51581.2021.9466265 http://dx.doi.org/10.1109/ICCP51581.2021.9466265 ]
Zhu A Z, Yuan L Z, Chaney K and Daniilidis K. 2018c. EV-FlowNet: self-supervised optical flow estimation for event-based cameras [EB/OL ] . [2022-02-15 ] . https://arxiv.org/pdf/1802.06898.pdf https://arxiv.org/pdf/1802.06898.pdf
Zhu A Z, Yuan L Z, Chaney K and Daniilidis K. 2019a. Live demonstration: unsupervised event-based learning of optical f low, depth and egomotion//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach, USA: IEEE: 1694-1694 [ DOI: 10.1109/CVPRW.2019.00216 http://dx.doi.org/10.1109/CVPRW.2019.00216 ]
Zhu L, Dong S W, Huang T J and Tian Y H. 2019b. A retina-inspired sampling method for visual texture reconstruction//Proceedings of 2019 IEEE International Conference on Multimedia and Expo. Shanghai, China: IEEE: 1432-1437 [ DOI: 10.1109/ICME.2019.00248 http://dx.doi.org/10.1109/ICME.2019.00248 ]
Zhu L, Dong SW, Li J N, Huang T J and Tian Y H. 2020. Retina-like visual image reconstruction via spiking neural model//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE: 1435-1443 [ DOI: 10.1109/CVPR42600.2020.00151 http://dx.doi.org/10.1109/CVPR42600.2020.00151 ]
Zhu L, Li J N, Wang X, Huang T J and Tian Y H. 2021. NeuSpike-Net: high speed video reconstruction via bio-inspired neuromorphic cameras//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal, Canada: IEEE: 2380-2389 [ DOI: 10.1109/ICCV48922.2021.00240 http://dx.doi.org/10.1109/ICCV48922.2021.00240 ]
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