• Survey paper

• Workshops

• Devices and Manufacturers

• Companies working on Event-based Vision

• Neuromorphic Systems

• Review papers

  • Bio-inspiration
  • Algorithms, Applications

• Applications / Algorithms

  • Feature Detection and Tracking
    • Corners
    • Particles in fluids
  • Optical Flow Estimation
    • Scene Flow Estimation
  • Reconstruction of Visual Information
    • Intensity-Image Reconstruction
    • Video Synthesis
    • Image super-resolution
    • Joint/guided filtering
    • Tone mapping
    • Visual Stabilization
    • Polarization Reconstruction
  • Depth Estimation (3D Reconstruction)
    • Monocular
    • Stereo
    • Stereoscopic panoramic imaging
  • SLAM (Simultaneous Localization And Mapping)
    • Localization, Ego-motion estimation
    • Visual Odometry
    • Visual-Inertial Odometry
  • Segmentation
    • Object Segmentation
    • Motion Segmentation
  • Pattern recognition
    • Object Recognition
    • Gesture Recognition
    • Representation / Feature Extraction
    • Regression Tasks
    • Learning Methods / Frameworks
  • Signal Processing
    • Event Denoising
    • Compression
  • Control
  • Obstacle Avoidance
  • Space Applications
  • Tactile Sensing Applications
  • Object Pose Estimation
    • Human Pose Estimation
    • Hand Pose Estimation
  • Indoor Lighting Estimation
  • Data Encryption
  • Nuclear Verification
  • Optical Communication
  • Animal Behavior Monitoring
  • Optical Applications
    • Auto-focus
    • Speckle Analysis
    • Interferometry or Holography
    • Wavefront sensing
    • Optical super-resolution

• Datasets and Simulators

• Software

  • Drivers
  • Synchronization
  • Lens Calibration
  • Algorithms
  • Utilities

• Neuromorphic Processors and Platforms

• Courses

• Theses and Dissertations

  • Dissertations
  • Master's Theses

• People / Organizations

• EETimes articles

• Contributing

• Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A., Conradt, J., Daniilidis, K., Scaramuzza, D.,
  Event-based Vision: A Survey,
  IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2020.

• CVPR 2023 Fourth International Workshop on Event-based Vision, Videos
• IISW 2023 Int. Image Sensor Workshop
• MFI 2022 First Neuromorphic Event Sensor Fusion Workshop with videos incl. Event Sensor Fusion Jeopardy game - Virtual. Videos
• tinyML Neuromorphic Engineering Forum - Virtual, 2022. Videos
• CVPR 2021 Third International Workshop on Event-based Vision - Virtual. Videos
• ICRA 2020 Workshop on Unconventional Sensors in Robotics - Virtual. Videos
• Neuro-Inspired Computational Elements (NICE) Workshop Series. Videos
• IEEE Embedded Vision Workshop Series, with focus on Biologically-inspired vision and embedded systems.
• Capo Caccia Workshops toward Cognitive Neuromorphic Engineering.
• The Telluride Neuromorphic Cognition Engineering Workshops. Videos, Telluride 2020 (Online): Videos, Slides
• CVPR 2019 Second International Workshop on Event-based Vision and Smart Cameras. Videos
• IROS 2018 Unconventional Sensing and Processing for Robotic Visual Perception.
• ICRA 2017 First International Workshop on Event-based Vision. Videos
• IROS 2015 Event-Based Vision for High-Speed Robotics (slides), Workshop on Alternative Sensing for Robot Perception.
• ICRA 2015 Workshop on Innovative Sensing for Robotics, with a focus on Neuromorphic Sensors.

• DVS (Dynamic Vision Sensor): Lichtsteiner, P., Posch, C., and Delbruck, T., A 128x128 120dB 15μs latency asynchronous temporal contrast vision sensor, IEEE J. Solid-State Circuits, 43(2):566-576, 2008. PDF
  • Product page at iniVation. Buy a DVS
  • Product specifications
  • User guide
  • Introductory videos about the DVS technology
  • iniVation AG invents, produces and sells neuromorphic technologies with a special focus on event-based vision into business. Slides by S. E. Jakobsen, board member of iniVation.
  • Event Cameras - Tutorial - Tobi Delbruck, version 4
• Samsung's DVS
  • Slides and Video by Hyunsurk Eric Ryu, Samsung Electronics (2019).
  • Suh et al., A 1280×960 Dynamic Vision Sensor with a 4.95-μm Pixel Pitch and Motion Artifact Minimization, IEEE Int. Symp. Circuits and Systems (ISCAS), 2020.
  • Son, B., et al., A 640×480 dynamic vision sensor with a 9µm pixel and 300Meps address-event representation, IEEE Int. Solid-State Circuits Conf. (ISSCC), 2017, pp. 66-67.
  • SmartThings Vision commercial product for home monitoring. in Australia
  • Paper at IEDM 2019, about low-latency applications using Samsung's VGA DVS.
• DAVIS (Dynamic and Active Pixel Vision Sensor) : Brandli, C., Berner, R., Yang, M., Liu, S.-C., Delbruck, T., A 240x180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor, IEEE J. Solid-State Circuits, 49(10):2333-2341, 2014. PDF
  • Product page at iniVation. Buy a DAVIS
  • Product specifications
  • User guide
  • Color-DAVIS: Li, C., Brandli, C., Berner, R., Liu, H., Yang, M., Liu, S.-C., Delbruck, T., Design of an RGBW Color VGA Rolling and Global Shutter Dynamic and Active-Pixel Vision Sensor, IEEE Int. Symp. Circuits and Systems (ISCAS), 2015, pp. 718-721.
  • SDAVIS192: Moeys, D. P., Corradi, F., Li, C., Bamford, S. A., Longinotti, L., Voigt, F. F., Berry, S., Taverni, G., Helmchen, F., Delbruck, T., A Sensitive Dynamic and Active Pixel Vision Sensor for Color or Neural Imaging Applications, IEEE Trans. Biomed. Circuits Syst. 12(1):123-136 2018.
  • DAVIS346: Taverni, G; Paul Moeys, D; Li, C; Cavaco, C; Motsnyi, V; San Segundo Bello, D; Delbruck, T.,
    Front and Back Illuminated Dynamic and Active Pixel Vision Sensors Comparison,
    IEEE Trans. Circuits Syst. Express Briefs, 2018
• Insightness's Silicon Eye QVGA event sensor.
  • The Silicon Eye Technology
  • Slides and Video by Stefan Isler (2019).
  • Slides and Video by Christian Brandli, CEO and co-founder of Insightness (2017).
• PROPHESEE’s Metavision Sensor and Software
  • ATIS (Asynchronous Time-based Image Sensor): Posch, C., Matolin, D., Wohlgenannt, R. (2011). A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS, IEEE J. Solid-State Circuits, 46(1):259-275, 2011. PDF, YouTube, YouTube
  • Prophesee Gen4 is described in: Finateu et al., A 1280×720 Back-Illuminated Stacked Temporal Contrast Event-Based Vision Sensor with 4.86μm Pixels, 1.066GEPS Readout, Programmable Event-Rate Controller and Compressive Data-Formatting Pipeline, IEEE Int. Solid-State Circuits Conf. (ISSCC), 2020, pp. 112-114.
  • Buy a Prophesee packaged sensor VGA
  • Prophesee Cameras Specifications
  • What is event-based vision and sample applications, YouTube
  • Download free or buy our software
  • Documentation and tutorials
  • Knowledge Base and Community Forum
• SONY's explanation of Event-based Vision Sensor (EVS) Technolgy
• CelePixel, Shanghai. CeleX-V: the first 1 Mega-pixel event-camera sensor.
• Sensitive DVS (sDVS)
  • Leñero-Bardallo, J. A., Serrano-Gotarredona, T., Linares-Barranco, B., A 3.6us Asynchronous Frame-Free Event-Driven Dynamic-Vision-Sensor, IEEE J. of Solid-State Circuits, 46(6):1443-1455, 2011.
  • Serrano-Gotarredona, T. and Linares-Barranco, B., A 128x128 1.5% Contrast Sensitivity 0.9% FPN 3us Latency 4mW Asynchronous Frame-Free Dynamic Vision Sensor Using Transimpedance Amplifiers, IEEE J. Solid-State Circuits, 48(3):827-838, 2013.
• DLS (Dynamic Line Sensor): Posch, C., Hofstaetter, M., Matolin, D., Vanstraelen, G., Schoen, P., Donath, N., and Litzenberger, M., A dual-line optical transient sensor with on-chip precision time-stamp generation, IEEE Int. Solid-State Circuits Conf. - Digest of Technical Papers, Lisbon Falls, MN, US, 2007.
  • Fact sheet at AIT.
• LWIR DVS: Posch, C., Matolin, D., Wohlgenannt, R., Maier, T., Litzenberger, M., A Microbolometer Asynchronous Dynamic Vision Sensor for LWIR, IEEE Sensors Journal, 9(6):654-664, 2009.
  • Prototype, commercially n.a.
• Smart DVS (GAEP): Posch, C., Hoffstaetter, M., Schoen, P., A SPARC-compatible general purpose Address-Event processor with 20-bit 10ns-resolution asynchronous sensor data interface in 0.18um CMOS, IEEE Int. Symp. Circuits and Systems (ISCAS), 2010.
  • Prototype, commercially n.a.
• PDAVIS: Haessig, G. et al., Bio-inspired Polarization Event Camera,
  arXiv [cs.CV] (2021) PDAVIS video
  • Prototype, commercially n.a.
• Center Surround Event Camera (CSDVS): Delbruck, T., Li, C., Graca, R. & Mcreynolds, B.,
  Utility and Feasibility of a Center Surround Event Camera
  arXiv [cs.CV] (2022) CSDVS videos
  • Proposed architecture.

• iniVation AG invents, produces and sells neuromorphic technologies with a special focus on event-based vision into business.
• iniLabs AG invents neuromorphic technologies for research.
• Samsung develops Gen2 and Gen3 dynamic vision sensors and event-based vision solutions.
  • IBM Research (Synapse project) and Samsung partenered to combine the TrueNorth chip (brain) with a DVS (eye).
• Prophesee (Formerly Chronocam) is the inventor and supplier of 4 Event-Based sensors generations, including commercial-grade versions as well as industry’s largest software suite. The company focuses on Industrial, Mobile-IoT and Automotive applications.
• Insightness AG builds visual systems to give mobile devices spatial awareness. The Silicon Eye Technology.
• SLAMcore develops Localisation and mapping solutions for AR/VR, robotics & autonomous vehicles.
• CelePixel (formerly Hillhouse Technology) offer integrated sensory platforms that incorporate various components and technologies, including a processing chipset and an image sensor (a dynamic vision sensor called CeleX).
• AIT Austrian Institute of Technology sells neuromorphic sensor products.
  • Inspection during production of carton packs
  • UCOS Universal Counting Sensor
  • IVS Industrial Vision Sensor

• Serrano-Gotarredona, T. , Andreou, A.G. , Linares-Barranco, B.,
  AER Image Filtering Architecture for Vision Processing Systems,
  IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., 46(9):1064-1071, 1999.
• Serrano-Gotarredona, R., Oster, M., Lichtsteiner, P., Linares-Barranco, A., Paz-Vicente, R., Gomez-Rodriguez, F., Riis, H.K., Delbruck, T., Liu, S.-H., Zahnd, S., Whatley, A.M., Douglas, R., Hafliger, P., Jimenez-Moreno, G., Civit, A., Serrano-Gotarredona, T., Acosta-Jimenez, A., Linares-Barranco, B.,
  AER building blocks for multi-layer multi-chip neuromorphic vision systems,
  Advances in neural information processing systems, 1217-1224, 2006.
• Liu, S.-C. and Delbruck, T.,
  Neuromorphic sensory systems,
  Current Opinion in Neurobiology, 20:3(288-295), 2010.
• Zamarreño-Ramos, C., Linares-Barranco, A., Serrano-Gotarredona, T., Linares-Barranco, B.,
  Multi-Casting Mesh AER: A Scalable Assembly Approach for Reconfigurable Neuromorphic Structured AER Systems. Application to ConvNets,
  IEEE Trans. Biomed. Circuits Syst., 7(1):82-102, 2013.
• Liu, S.-C., Delbruck, T., Indiveri, G., Whatley, A., Douglas, R.,
  Event-Based Neuromorphic Systems,
  Wiley. ISBN: 978-1-118-92762-5, 2014.
• Chicca, E., Stefanini, F., Bartolozzi, C., Indiveri, G.,
  Neuromorphic Electronic Circuits for Building Autonomous Cognitive Systems,
  Proc. IEEE, 102(9):1367-1388, 2014.
• Vanarse, A., Osseiran, A., Rassau, A,
  A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors,
  Front. Neurosci. (2016), 10:115.
• Liu et al., Signal Process. Mag. 2019,
  Event-Driven Sensing for Efficient Perception: Vision and audition algorithms.
• Event Cameras Tutorial - Tobi Delbruck, version 4.1, Sep. 18, 2020.
• Kirkland, P., Di Caterina, G., Soraghan, J., Matich, G.,
  Neuromorphic technologies for defence and security,
  SPIE vol 11540, Emerging Imaging and Sensing Technologies for Security and Defence V; and Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence III; 2020.

• Delbruck, T.,
  Activity-driven, event-based vision sensors,
  IEEE Int. Symp. Circuits and Systems (ISCAS), 2010. PDF.
• Posch, C.,
  Bio-inspired vision,
  J. of Instrumentation, 7 C01054, 2012. Bio-inspired explanation of the DVS and the ATIS. PDF
• Posch, C., Serrano-Gotarredona, T., Linares-Barranco, B., Delbruck, T.,
  Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output,
  Proc. IEEE (2014), 102(10):1470-1484. PDF
• Posch, C.,
  Bioinspired vision sensing,
  Biologically Inspired Computer Vision, Wiley-Blackwell, pp. 11-28, 2015. book index
• Posch, C., Benosman, R., Etienne-Cummings, R.,
  How Neuromorphic Image Sensors Steal Tricks From the Human Eye, also published as Giving Machines Humanlike Eyes,
  IEEE Spectrum, 52(12):44-49, 2015. PDF
• Cho, D., Lee, T.-J.,
  A Review of Bioinspired Vision Sensors and Their Applications,
  Sensors and Materials, 27(6):447-463, 2015. PDF
• Sandamirskaya, Y., Kaboli, M., Conradt, J., Celikel, T.,
  Neuromorphic computing hardware and neural architectures for robotics,
  Science Robotics, 7(67):eabl8419, 2022.

• Delbruck, T.,
  Fun with asynchronous vision sensors and processing.
  Computer Vision - ECCV 2012. Workshops and Demonstrations. Springer Berlin/Heidelberg, 2012. A position paper and summary of recent accomplishments of the INI Sensors' group.
• Delbruck, T.,
  Neuromorophic Vision Sensing and Processing (Invited paper),
  46th Eur. Solid-State Device Research Conference (ESSDERC), Lausanne, 2016, pp. 7-14.
• Lakshmi, A., Chakraborty, A., Thakur, C.S.,
  Neuromorphic vision: From sensors to event-based algorithms,
  Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9(4), 2019.
• Steffen, L. et al., Front. Neurorobot. 2019,
  Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms.
• Gallego et al., TPAMI 2020,
  Event-based Vision: A Survey.
• Chen, G., Cao, H., Conradt, J., Tang, H., Rohrbein, F., Knoll, A.,
  Event-Based Neuromorphic Vision for Autonomous Driving: A Paradigm Shift for Bio-Inspired Visual Sensing and Perception,
  IEEE Signal Processing Magazine, 37(4):34-49, 2020.
• Chen, G., Wang, F., Li, W., Hong, L., Conradt, J., Chen, J., Zhang, Z., Lu, Y., Knoll, A.,
  NeuroIV: Neuromorphic Vision Meets Intelligent Vehicle Towards Safe Driving With a New Database and Baseline Evaluations,
  IEEE Trans. Intelligent Transportation Systems (TITS), 2020.
• Tayarani-Najaran, M.-H., Schmuker, M.,
  Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review,
  Front. Neural Circuits 15:610446, 2021.
• Sun, R. Shi, D., Zhang, Y., Li, R., Li, R.,
  Data-Driven Technology in Event-Based Vision,
  Complexity, vol. 2021, Article ID 6689337.
• Bartolozzi, C., Indiveri, G., Donati, E.,
  Embodied neuromorphic intelligence,
  Nat. Commun. 13:1024, 2022.
• Zou, XL., Huang, T.J., Wu, S.,
  Towards a New Paradigm for Brain-inspired Computer Vision,
  Mach. Intell. Res., 19:412-424, 2022.
• Gehrig, D., Scaramuzza, D.,
  Are High-Resolution Cameras Really Needed?,
  arXiv, 2022. YouTube, code.
• Ercan, B., Eker, O., Erdem, A., Erdem, E.
  EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2023. PDF, Project Page, Suppl., Code.

• Litzenberger, M., Posch, C., Bauer, D., Belbachir, A. N., Schon. P., Kohn, B., Garn, H.,
  Embedded Vision System for Real-Time Object Tracking using an Asynchronous Transient Vision Sensor,
  IEEE 12th Digital Signal Proc. Workshop and 4th IEEE Signal Proc. Education Workshop, Teton National Park, WY, 2006, pp. 173-178. PDF
  • Litzenberger, M., Kohn, B., Belbachir, A.N., Donath, N., Gritsch, G., Garn, H., Posch, C., Schraml, S.,
    Estimation of Vehicle Speed Based on Asynchronous Data from a Silicon Retina Optical Sensor,
    IEEE Intelligent Transportation Systems Conf. (ITSC), 2006, pp. 653-658. PDF
  • Bauer, D., Belbachir, A. N., Donath, N., Gritsch, G., Kohn, B., Litzenberger, M., Posch, C., Schön, P., Schraml, S.,
    Embedded Vehicle Speed Estimation System Using an Asynchronous Temporal Contrast Vision Sensor,
    EURASIP J. Embedded Systems, 2007:082174. PDF
  • Litzenberger, M., Belbachir, N., Schon, P., Posch, C.,
    Embedded Smart Camera for High Speed Vision,
    ACM/IEEE Int. Conf. on Distributed Smart Cameras, 2007. PDF
• Ni, Z., Bolopion, A., Agnus, J., Benosman, R., Regnier, S.,
  Asynchronous event-based visual shape tracking for stable haptic feedback in microrobotics,
  IEEE Trans. Robot. (TRO), 28(5):1081-1089, 2012. PDF
  • Ni, Ph.D. Thesis, 2013,
    Asynchronous Event Based Vision: Algorithms and Applications to Microrobotics.
  • Ni, Z., Ieng, S. H., Posch, C., Regnier, S., Benosman, R.,
    Visual Tracking Using Neuromorphic Asynchronous Event-Based Cameras,
    Neural Computation (2015), 27(4):925-953. PDF, YouTube
• Piatkowska, E., Belbachir, A. N., Schraml, S., Gelautz, M.,
  Spatiotemporal multiple persons tracking using Dynamic Vision Sensor,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012, pp. 35-40. PDF
• Lagorce, X., Ieng, S.-H., Clady, X., Pfeiffer, M., Benosman, R.,
  Spatiotemporal features for asynchronous event-based data,
  Front. Neurosci. (2015), 9:46.
  • Lagorce, X., Ieng, S. H., Benosman, R.,
    Event-based features for robotic vision,
    IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2013, pp. 4214-4219.
• Saner, D., Wang, O., Heinzle, S., Pritch, Y., Smolic, A., Sorkine-Hornung, A., Gross, M.,
  High-Speed Object Tracking Using an Asynchronous Temporal Contrast Sensor,
  Int. Symp. Vision, Modeling and Visualization (VMV), 2014. PDF
• Lagorce, X., Meyer, C., Ieng, S. H., Filliat, D., Benosman, R.,
  Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking,
  IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 26(8):1710-1720, 2015. PDF, YouTube
  • Lagorce, X., Meyer, C., Ieng, S. H., Filliat, D., Benosman, R.,
    Live demonstration: Neuromorphic event-based multi-kernel algorithm for high speed visual features tracking,
    IEEE Biomedical Circuits and Systems Conference (BioCAS), 2014, pp. 178.
• Reverter Valeiras, D., Lagorce, X., Clady, X., Bartolozzi, C., Ieng, S., Benosman, R.,
  An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking,
  IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 26(12):3045-3059, 2015. PDF, YouTube
• Linares-Barranco, A., Gómez-Rodríguez, F., Villanueva, V., Longinotti, L., Delbrück, T.,
  A USB3.0 FPGA event-based filtering and tracking framework for dynamic vision sensors,
  IEEE Int. Symp. Circuits and Systems (ISCAS), 2015.
• Leow, H. S., Nikolic, K.,
  Machine vision using combined frame-based and event-based vision sensor,
  IEEE Int. Symp. Circuits and Systems (ISCAS), 2015.
• Liu, H., Moeys, D. P., Das, G., Neil, D., Liu, S.-C., Delbruck, T.,
  Combined frame- and event-based detection and tracking,
  IEEE Int. Symp. Circuits and Systems (ISCAS), 2016.
• Tedaldi, D., Gallego, G., Mueggler, E., Scaramuzza, D.,
  Feature detection and tracking with the dynamic and active-pixel vision sensor (DAVIS),
  IEEE Int. Conf. Event-Based Control Comm. and Signal Proc. (EBCCSP), 2016. PDF, YouTube
  • Kueng et al., IROS 2016 Low-Latency Visual Odometry using Event-based Feature Tracks.
• Braendli, C., Strubel, J., Keller, S., Scaramuzza, D., Delbruck, T.,
  ELiSeD - An Event-Based Line Segment Detector,
  Int. Conf. on Event-Based Control Comm. and Signal Proc. (EBCCSP), 2016. PDF
• Glover, A. and Bartolozzi, C.,
  Event-driven ball detection and gaze fixation in clutter,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2016, pp. 2203-2208. YouTube, Code
  • Glover, A. and Bartolozzi, C.,
    Robust Visual Tracking with a Freely-moving Event Camera,
    IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2017. YouTube, Code
  • Glover, A., Stokes, A.B., Furber, S., Bartolozzi, C.,
    ATIS + SpiNNaker: a Fully Event-based Visual Tracking Demonstration,
    IEEE/RSJ Int. Conf. Intelligent Robots and Systems Workshops (IROSW), 2018. Workshop on Unconventional Sensing and Processing for Robotic Visual Perception.
• Clady, X., Maro, J.-M., Barré, S., Benosman, R. B.,
  A Motion-Based Feature for Event-Based Pattern Recognition.
  Front. Neurosci. (2017), 10:594. PDF
• Zhu, A., Atanasov, N., Daniilidis, K.,
  Event-based Feature Tracking with Probabilistic Data Association,
  IEEE Int. Conf. Robotics and Automation (ICRA), 2017. PDF, YouTube, Code
• Barrios-Avilés, J., Iakymchuk, T., Samaniego, J., Medus, L.D., Rosado-Muñoz, A.,
  Movement Detection with Event-Based Cameras: Comparison with Frame-Based Cameras in Robot Object Tracking Using Powerlink Communication,
  Electronics 2018, 7, 304. PDF pre-print
• Li, J., Shi, F., Liu, W., Zou, D., Wang, Q., Park, P.K.J., Ryu, H.,
  Adaptive Temporal Pooling for Object Detection using Dynamic Vision Sensor,
  British Machine Vision Conf. (BMVC), 2017.
• Peng, X., Zhao, B., Yan, R., Tang H., Yi, Z.,
  Bag of Events: An Efficient Probability-Based Feature Extraction Method for AER Image Sensors,
  IEEE Trans. Neural Netw. Learn. Syst. (TNNLS), 28(4):791-803, 2017.
• Ramesh, B., Yang, H., Orchard, G., Le Thi, N.A., Xiang, C,
  DART: Distribution Aware Retinal Transform for Event-based Cameras,
  IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2019. PDF
• Gehrig, D., Rebecq, H., Gallego, G., Scaramuzza, D.,
  EKLT: Asynchronous, Photometric Feature Tracking using Events and Frames,
  Int. J. Computer Vision (IJCV), 2019. YouTube, Tracking code, Evaluation code
  • Gehrig, D., Rebecq, H., Gallego, G., Scaramuzza, D.,
    Asynchronous, Photometric Feature Tracking using Events and Frames,
    European Conf. Computer Vision (ECCV), 2018. Poster, YouTube, Oral presentation, Tracking code, Evaluation code
• Everding, L., Conradt, J.,
  Low-Latency Line Tracking Using Event-Based Dynamic Vision Sensors,
  Front. Neurorobot. 12:4, 2018. Videos
• Linares-Barranco, A., Liu, H., Rios-Navarro, A., Gomez-Rodriguez, F., Moeys, D., Delbruck, T.
  Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics,
  Entropy 2018, 20(6), 475.
• Mitrokhin, A., Fermüller, C., Parameshwara, C., Aloimonos, Y.,
  Event-based Moving Object Detection and Tracking,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2018. PDF, YouTube, Project page and Dataset
• Iacono, M., Weber, S., Glover, A., Bartolozzi, C.,
  Towards Event-Driven Object Detection with Off-The-Shelf Deep Learning,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2018.
• Ramesh, B., Zhang, S., Lee, Z.-W., Gao, Z., Orchard, G., Xiang, C.,
  Long-term object tracking with a moving event camera,
  British Machine Vision Conf. (BMVC), 2018. Video
  • Ramesh, B., Zhang, S., Yang, H., Ussa, A., Ong, M., Orchard, G., Xiang, C.,
    e-TLD: Event-based Framework for Dynamic Object Tracking,
    arXiv, 2020.
• Dardelet, L., Ieng, S.-H., Benosman, R.,
  Event-Based Features Selection and Tracking from Intertwined Estimation of Velocity and Generative Contours,
  arXiv:1811.07839, 2018.
• Wu, J., Zhang, K., Zhang, Y., Xie, X., Shi, G.,
  High-Speed Object Tracking with Dynamic Vision Sensor,
  China High Resolution Earth Observation Conference (CHREOC), 2018.
• Huang, J., Wang, S., Guo, M., Chen, S.,
  Event-Guided Structured Output Tracking of Fast-Moving Objects Using a CeleX Sensor,
  IEEE Trans. Circuits Syst. Video Technol. (TCSVT), 28(9):2413-2417, 2018.
• Renner, A., Evanusa, M., Sandamirskaya, Y.,
  Event-based attention and tracking on neuromorphic hardware,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. Video pitch
• Foster, B.J., Ye, D.H., Bouman, C.A.,
  Multi-target tracking with an event-based vision sensor and a partial-update GMPHD filter,
  IS&T International Symposium on Electronic Imaging 2019. Computational Imaging XVII.
• Alzugaray, I., Chli, M.,
  Asynchronous Multi-Hypothesis Tracking of Features with Event Cameras,
  IEEE Int. Conf. 3D Vision (3DV), 2019. PDF, Code, YouTube
• Linares-Barranco, A., Perez-Pena, F., Moeys, D.P., Gomez-Rodriguez, F., Jimenez-Moreno, G., Delbruck, T.
  Low Latency Event-based Filtering and Feature Extraction for Dynamic Vision Sensors in Real-Time FPGA Applications,
  IEEE Access, 7:134926-134942, 2019. Code
• Li, K., Shi, D., Zhang, Y., Li, R., Qin, W., Li, R.,
  Feature Tracking Based on Line Segments With the Dynamic and Active-Pixel Vision Sensor (DAVIS),
  IEEE Access, 7:110874-110883, 2019.
• Bolten T., Pohle-Fröhlich R., Tönnies K.D.,
  Application of Hierarchical Clustering for Object Tracking with a Dynamic Vision Sensor,
  Int. Conf. Computational Science (ICCS) 2019. PDF
• Chen, H., Wu, Q., Liang, Y., Gao, X., Wang, H.,
  Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking,
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  Neurocomputing, 547, 2023. Code

• Rudnev, V., Golyanik, V., Wang, J., Seidel, H.-P., Mueller, F., Elgharib, M., Theobalt, C.,
  EventHands: Real-Time Neural 3D Hand Reconstruction from an Event Stream,
  arXiv, 2020. Project page
• Xue, Y., Li, H., Leutenegger, S., Stueckler, J.,
  Event-based Non-Rigid Reconstruction from Contours,
  British Machine Vision Conf. (BMVC), 2022. Project page

• Chen, Z., Zheng, Q., Niu, P., Tang, H., Pan, G.,
  Indoor Lighting Estimation Using an Event Camera ,
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2021. Suppl.

• Du, B., Li, W., Wang, Z., Xu, M., Gao, T., Li, J., Wen, H.,
  Event Encryption for Neuromorphic Vision Sensors: Framework, Algorithm, and Evaluation,
  Sensors, 2021.
• Zhang, P., Zhu, S., Lam, E. Y.,
  Event Encryption: Rethinking Privacy Exposure for Neuromorphic Imaging,
  arXiv, 2023.

• A. Glaser,
  Keeping Secrets at a Distance: New Approaches to Nuclear Monitoring and Verification,
  Distinguished Lecture, Cyber Security in the Age of Large-Scale Adversaries (CASA), Ruhr-Universität Bochum, Germany, 2022.

• Wang, Z., Ng, Y., Henderson, J., Mahony., R.,
  Smart Visual Beacons with Asynchronous Optical Communications using Event Cameras,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2022. PDF, Code and Data.

• Hamann, F., Gallego, G.,
  Stereo Co-capture System for Recording and Tracking Fish with Frame- and Event Cameras,
  26th Int. Conf. Pattern Recognition (ICPR), Visual observation and analysis of Vertebrate And Insect Behavior (VAIB) Workshop, Montreal, Canada, 2022.

• Lin, S., Zhang, Y., Zhang, L., Zhou, B., Luo, X., Pan, J.,
  Autofocus for Event Cameras,
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2022. PDF, Suppl..
• Ge, Z., Wei, H., Xu, F., Gao, Y., Chu, Z., So, H., Lam, E.,
  Millisecond autofocusing microscopy using neuromorphic event sensing,
  Optics and Lasers in Engineering, 2023.
• Bao et al., Arxiv 2023,
  Improving Fast Auto-Focus with Event Polarity.
• Lou, H., Teng, M., Yang, Y., Shi, B.,
  All-in-Focus Imaging From Event Focal Stack,
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2023. PDF
• Bao, Y., Sun, L., Ma, Y., Gu, D. & Wang, K.,
  Improving Fast Auto-Focus with Event Polarity,
  Opt. Express, 2023.

• Ge, Z., Meng, N., Song, L, Lam, E.,
  Dynamic laser speckle analysis using the event sensor,
  Applied Optics, 2021.
• Ge, Z., Gao, Y., So, H., Lam, E.,
  Event-based laser speckle correlation for micro motion estimation,
  Optics Letters, 2021.
• Ge, Z., Zhang, P., Gao, Y., So, H, Lam, E.,
  Lens-free motion analysis via neuromorphic laser speckle imaging,
  Optics Express, 2022.

• Schober, C., Pruss, C., Faulhaber, A., Herkommer, A.,
  Event based coherence scanning interferometry,
  Optics Letters, 2021.

• Kong, F., Lambert, A., Joubert, D., Cohen, G.,
  Shack-Hartmann wavefront sensing using spatial-temporal data from an event-based image sensor,
  Optics Express, 2020.

• Cabriel, C., Specht, C. G. & Izeddin, I.,
  Event-based vision sensor enables fast and dense single-molecule localization microscopy,
  bioArxiv, 2022.
• Mangalwedhekar, R., Singh, N., Thakur, C. S., Seelamantula, C. S., Jose, M., and Nair, D.,
  Achieving nanoscale precision using neuromorphic localization microscopy,
  Nat. Nanotechnol., 2023.

• Katz, M. L., Nikolic, K., Delbruck, T. (2012),
  Live demonstration: Behavioural emulation of event-based vision sensors,
  IEEE Int. Symp. Circuits and Systems (ISCAS), 2012. PDF
• Kaiser, J., Tieck, J. C. V., Hubschneider, C., Wolf, P., Weber, M., Hoff, M., Friedrich., A., Wojtasik, K., Roennau, A., Kohlhaas, R., Dillmann, R., Zoellner, M. (2016),
  Towards a framework for end-to-end control of a simulated vehicle with spiking neural networks,
  IEEE Int. Conf. on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), 2016. PDF, Gazebo DVS plugin
• Pineda García, G., Camilleri, P., Liu, Q., Furber, S.,
  pyDVS: An extensible, real-time Dynamic Vision Sensor emulator using off-the-shelf hardware,
  IEEE Int. Symp. Series on Computational Intelligence (SSCI), 2016. Code
• Mueggler et al., IJRR 2017.
  The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.
• Bi, Y. and Andreopoulos, Y.,
  PIX2NVS: Parameterized conversion of pixel-domain video frames to neuromorphic vision streams,
  IEEE Int. Conf. Image Processing (ICIP), 2017, GitHub Page.
• Li, W., Saeedi, S., McCormac, J., Clark, R., Tzoumanikas, D., Ye, Q., Huang, Y., Tang, R., Leutenegger, S.,
  Interiornet: Mega-scale multi-sensor photo-realistic indoor scenes dataset,
  British Machine Vis. Conf. (BMVC), 2018. YouTube, Project Page.
• Rebecq, H., Gehrig, D., Scaramuzza, D.,
  ESIM: an Open Event Camera Simulator,
  Conf. on Robot Learning (CoRL), 2018. PDF, YouTube, Project Page.
• Gehrig et al. CVPR 2020,
  Video to Events: Recycling Video Datasets for Event Cameras.
• Hu, Y., S.-C., Liu, Delbruck, T.,
  v2e: From Video Frames to Realistic DVS Events,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. Project page, YouTube, Suppl., Code.
• Nehvi, J., Golyanik, V., Mueller, F., Seidel, H.-P., Elgharib, M., Theobalt, C.,
  Differentiable Event Stream Simulator for Non-Rigid 3D Tracking,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. Project page, YouTube, Suppl..
• Joubert, C., Marcireau, A., Ralph, N., Jolley, A., van Schaik, A., Cohen, G.,
  Event Camera Simulator Improvements via Characterized Parameters
  Front. Neurosci., 2021, IEBCS simulator

• Datasets from the Sensors group at INI (Institute of Neuroinformatics), Zurich:
  • DVS09 - DVS128 Dynamic Vision Sensor Silicon Retina
  • DVSFLOW16 - DVS/DAVIS Optical Flow Dataset
  • DVSACT16 - DVS Datasets for Object Tracking, Action Recognition and Object Recognition
  • PRED18 - VISUALISE Predator/Prey Dataset
  • DDD17 - DAVIS Driving Dataset 2017
  • ROSHAMBO17 - RoShamBo Rock Scissors Paper game DVS dataset
  • DHP19 - DAVIS Human Pose Estimation and Action Recognition
  • DDD20 - End-to-End Event Camera Driving Dataset
  • DND21 - DeNoising Dynamic vision sensors dataset
  • EDFLOW21 - Event Driven Flow dataset

• Calabrese et al., CVPRW 2019,
  DHP19: Dynamic Vision Sensor 3D Human Pose Dataset.
• Zhang et al., Neurocomputing 2023,
  Neuromorphic high-frequency 3D dancing pose estimation in dynamic environment.

• Andreopoulos et al., CVPR 2018, A Low Power, High Throughput, Fully Event-Based Stereo System.
• Zhu et al., RAL 2018: MVSEC The Multi Vehicle Stereo Event Camera Dataset.
• Zhou et al., ECCV 2018: Semi-Dense 3D Reconstruction with a Stereo Event Camera.
• Zhou et al., TRO 2021, Event-based Stereo Visual Odometry.
• Gehrig, M., Aarents, W., Gehrig, D., Scaramuzza, D.,
  DSEC: A Stereo Event Camera Dataset for Driving Scenarios,
  IEEE Robotics and Automation Letters (RA-L), 2021. Dataset, PDF, Code, Video.
• Wang et al., IROS 2021 (SHEF), Stereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception, Project Page.
• Gao et al., RAL 2022, VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM.

• Chen et al., ICVR 2023, Dense Voxel 3D Reconstruction Using a Monocular Event Camera.

• DVS/DAVIS Optical Flow Dataset associated to the paper Rueckauer and Delbruck, FNINS 2016.
• Bardow et al., CVPR2016, Four sequences
• Zhu et al., RAL2018: MVSEC The Multi Vehicle Stereo Event Camera Dataset.
• Almatrafi et al. PAMI 2020: Distance Surface for Event-Based Optical Flow. DVSMOTION20 Dataset
• EDFLOW21 Event Driven Optical Flow Camera dataset associated with the paper EDFLOW: Event Driven Optical Flow Camera with Keypoint Detection and Adaptive Block Matching.
• Gehrig et al., RAL 2021: DSEC: A Stereo Event Camera Dataset for Driving Scenarios.
• EV-IMO Event based Independently Moving Objects dataset associated to the paper EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras (motion vector flow added Jan 2022)

• Bardow et al., CVPR2016, Four sequences
• Scheerlinck et al., ACCV2018, Continuous-time Intensity Estimation Using Event Cameras. Website
• Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N., Mahony, R., Scaramuzza, D.,
  CED: Color Event Camera Dataset,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. Slides, Video pitch.
• Rebecq et al., TPAMI 2020,
  High Speed and High Dynamic Range Video with an Event Camera. Project page
• High Quality Frames (HQF) dataset associated to the paper Stoffregen et al., arXiv 2020.
• Wang et al., CVPR 2020,
  Joint Filtering of Intensity Images and Neuromorphic Events for High-Resolution Noise-Robust Imaging. Project page
• HDR Hybrid Event-Frame Dataset, TPAMI 2023,
  An Asynchronous Linear Filter Architecture for Hybrid Event-Frame Cameras. Project page

• Combined Dynamic Vision / RGB-D Dataset associated to the paper Weikersdorfer et al., ICRA 2014.
• Barranco, F., Fermüller, C., Aloimonos, Y.,
  A Dataset for Visual Navigation with Neuromorphic Methods,
  Front. Neurosci. (2016), 10:49.
• Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T., Scaramuzza, D.,
  The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM,
  Int. J. Robotics Research, 36:2, pp. 142-149, 2017. PDF, PDF IJRR, YouTube, Dataset.
• Binas, J., Neil, D., Liu, S.-C., Delbruck, T.,
  DDD17: End-To-End DAVIS Driving Dataset,
  Int. Conf. Machine Learning, Workshop on Machine Learning for Autonomous Vehicles, 2017. Dataset
• Zhu, A., Thakur, D., Ozaslan, T., Pfrommer, B., Kumar, V., Daniilidis, K.,
  The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception,
  IEEE Robotics and Automation Letters (RA-L), 3(3):2032-2039, Feb. 2018. PDF, Dataset, YouTube.
• Event-based, 6-DOF Camera Tracking from Photometric Depth Maps associated to the paper Gallego et al., PAMI 2018
• Leung, S., Shamwell, J., Maxey, C., Nothwang, W. D.,
  Toward a large-scale multimodal event-based dataset for neuromorphic deep learning applications,
  Proc. SPIE 10639, Micro- and Nanotechnology Sensors, Systems, and Applications X, 106391T. PDF
• Event-based, Direct Camera Tracking from a Photometric 3D Map using Nonlinear Optimization associated to the paper Bryner et al., ICRA 2019.
• Delmerico, J., Cieslewski, T., Rebecq, H., Faessler, M., Scaramuzza, D.,
  Are We Ready for Autonomous Drone Racing? The UZH-FPV Drone Racing Dataset,
  IEEE Int. Conf. Robotics and Automation (ICRA), 2019. PDF, YouTube, Project page.
• Lee, A. J., Cho, Y., Yoon, S., Shin, Y., Kim, A.,
  ViViD: Vision for Visibility Dataset,
  IEEE Int. Conf. Robotics and Automation (ICRA) Workshop: Dataset Generation and Benchmarking of SLAM Algorithms for Robotics and VR/AR, 2019.
• Mitrokhin et al., IROS 2019.
  EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
• Hu, Y., Binas, J., Neil, D., Liu, S.-C., Delbruck, T.,
  DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction,
  IEEE Intelligent Transportation Systems Conf. (ITSC), 2020. Dataset, More datasets
• Rodríguez-Gómez, J. P., Tapia, R., Paneque, J. L., Grau, P., Gómez Eguíluz, A., Martínez-de Dios, J. R., Ollero A.,
  The GRIFFIN Perception Dataset: Bridging the Gap Between Flapping-Wing Flight and Robotic Perception,
  IEEE Robotics and Automation Letters (RA-L), 2021.
• Klenk S., Chui, J., Demmel, N., Cremers, D.,
  TUM-VIE: The TUM Stereo Visual-Inertial Event Data Set,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021.
• Lee, A. J., Cho, Y., Shin, Y., Kim, A., Myung, H.,
  ViViD++: Vision for Visibility Dataset,
  IEEE Robotics and Automation Letters (RA-L), 2022. Dataset
• Gao, L., Liang, Y., Yang, J., Wu, S., Wang, C., Chen, J., Kneip, L.,
  VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM,
  IEEE Robotics and Automation Letters (RA-L), 7(3):8217-8224, 2022. PDF, Dataset, MPL Calibration Toolbox, MPL Dataset Toolbox.

• Mitrokhin et al., IROS 2018, Extreme Event Dataset (EED). Project page and Dataset
• Mitrokhin, A., Ye, C., Fermüller, C., Aloimonos, Y., Delbrück, T.,
  EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2019. PDF, Dataset, Project page

• Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.,
  Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades,
  Front. Neurosci. (2015), 9:437. YouTube
  • Neuromorphic-MNIST (N-MNIST) dataset is a spiking version of the original frame-based MNIST dataset (of handwritten digits). YouTube
  • The Neuromorphic-Caltech101 (N-Caltech101) dataset is a spiking version of the original frame-based Caltech101 dataset. YouTube
• Serrano-Gotarredona,T. and Linares-Barranco, B.,
  Poker-DVS and MNIST-DVS. Their History, How They were Made, and Other Details,
  Front. Neurosci. (2015), 9:481.
  • MNIST-DVS and FLASH-MNIST-DVS datasets are based on the original frame-based MNIST dataset. MNIST-DVS are DVS128 recordings of moving MNIST digits (at 3 scales), while FLASH-MNIST-DVS datasets are recorded by flashing the digits on a monitor.
  • POKER-DVS. From a set of DVS recordings of very fast poker card browsing, 32x32 pixel windows tracking the symbols are cropped. On average each symbol lasts about 10-30ms.
  • SLOW-POKER-DVS. Paper printed poker card symbols are moved at "human speed" in front of a DVS camera and recorded at 128x128 resolution.
• VISUALISE Predator/Prey Dataset associated to the paper Moeys et al., EBCCSP 2016
• Hu, Y., Liu, H., Pfeiffer, M., Delbruck, T.,
  DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition,
  Front. Neurosci. (2016), 10:405. Dataset
• Liu, Q., Pineda-García, G., Stromatias, E., Serrano-Gotarredona, T., Furber, SB.,
  Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation,
  Front. Neurosci. (2016), 10:496. Dataset, Dataset
• DVS128 Gesture Dataset: The dataset that was used to build the real-time gesture recognition system described in Amir et al., CVPR 2017.
• N-CARS Dataset: A large real-world event-based dataset for car classification. Sironi et al., CVPR 2018.
• Mitrokhin et al., IROS 2018 Event-based Moving Object Detection and Tracking. Project page and Dataset
• ATIS Plane Dataset, assocated to the paper Afshar et al., Front. Neurosci. 2018.
• Cheng, W., Luo, H., Yang, W., Yu, L., Chen, S., Li, W.,
  DET: A High-resolution DVS Dataset for Lane Extraction,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2019. Project page.
• Miao, S., Chen, G., Ning, X., Zi, Y., Ren, K., Bing, Z., Knoll, A.,
  Neuromorphic Vision Datasets for Pedestrian Detection, Action Recognition, and Fall Detection,
  Front. Neurorobot. (2019). Dataset
• de Tournemire, P., Nitti, D., Perot, E., Migliore, D., Sironi, A.,
  A Large Scale Event-based Detection Dataset for Automotive,
  arXiv, 2020. Code, News
• N-SOD Dataset associated to the paper Ramesh et al., FNINS 2020.
• SL-ANIMALS-DVS Database associated to the paper Vasudevan et al., FG 2020. Recordings made using the sensitive DVS developed at IMSE.
• Perot, E., de Tournemire, P., Nitti, D., Masci, J., Sironi, A., 1Mpx Detection Dataset: Learning to Detect Objects with a 1 Megapixel Event Camera. NeurIPS 2020.
• Cannici, M., Plizzari, C., Planamente, M., Ciccone, M., Bottino, A., Caputo, B., Matteucci, M.,
  N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain Adaptation,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. Project page, YouTube, Poster.
• N-EPIC-Kitchens Dataset of Plizzari et al., CVPR 2022. Project page
• THU^E-ACT-50 Dataset associated to the paper Gao et al., TPAMI 2023.

• DVSNOISE20 associated to the paper Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras.
• DND21 DeNoising Dynamic vision sensors dataset associated to the paper Low Cost and Latency Event Camera Background Activity Denoising
• Event Flicker Removal Dataset associated to the paper Wang et al., ICRA 2022. PDF, Project Page.

• The Event-Based Space Situational Awareness (EBSSA) Dataset associated to the paper Event-based Object Detection and Tracking for Space Situational Awareness.
• The Event Based Space Imaging Slew Speed Star Dataset associated to the paper Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging.

• Bolten, T., Pohle-Frohlich, R., Tonnies, K. D.,
  DVS-OUTLAB: A Neuromorphic Event-Based Long Time Monitoring Dataset for Real-World Outdoor Scenarios,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. Project page, YouTube

• jAER (java Address-Event Representation) project. Real time sensory-motor processing for event-based sensors and systems. github page. Wiki
• caer (AER event-based framework, written in C, targeting embedded systems)
• libcaer (Minimal C library to access, configure and get/send AER data from sensors or to/from neuromorphic processors)
• evl (Open Source Computer Vision Library for Event-based camera and vision for C++)
• ROS (Robotic Operating System)
• YARP (Yet Another Robot Platform)
• Prophesee ROS Wrapper ROS driver and messages for Prophesee event-based sensors
• Prophesee camera plugins
• CeleX5 ROS Wrapper A ROS driver and some other tools for CeleX5_MP event-based sensor (which has a high resolution at 1280×800)

• Sync Toolbox. This open-source toolbox provides a QT-based GUI to allow easy access for hardware-level multi-sensor synchronization (Prophesee Gen 3.1 included and tested). After proper configuration of the software, users can seamlessly record new ROS bags.

• Lens focus adjustment or this other source.
• For the DAVIS: use the grayscale frames to calibrate the optics of both frames and events.
  • ROS camera calibrator (monocular or stereo)
  • Kalibr software by ASL - ETH.
  • Basalt software by TUM.
• For the DAVIS camera and IMU calibration: kalibr software by ASL - ETH, using the grayscale frames.
• For the DVS (events-only):
  • Calibration using blinking LEDs or computer screens by RPG - UZH.
  • DVS camera calibration by G. Orchard.
  • DVS camera calibration by VLOGroup at TU Graz.
• For Prophesee Camera (events-only):
  • Focus adjustment and calibration using blinking LEDs or computer screens
• Song, R., Jiang, Z., Li, Y., Shan, Y., Huang, K.,
  Calibration of Event-based Camera and 3D LiDAR,
  WRC Symposium on Advanced Robotics and Automation (WRC SARA), 2018.
• Dominguez-Morales, M. J., Jimenez-Fernandez, A., Jimenez-Moreno, G., Conde, C., Cabello, E., Linares-Barranco, A.,
  Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors,
  IEEE Access, 7:138415-138425, 2019.
• Wang, Z., Ng, Y., van Goor, P., Mahony., R.,
  Event Camera Calibration of Per-pixel Biased Contrast Threshold,
  Australasian Conf. Robotics and Automation (ACRA) 2019. PDF, Code and Data.
• Dubeau, E., Garon, M., Debaque, B., de Charette, R., Lalonde, J.-F.,
  RGB-DE: Event Camera Calibration for Fast 6-DOF Object Tracking,
  arXiv, 2020.
• Wang, G., Feng, C., Hu, X., Yang, H.,
  Temporal Matrices Mapping Based Calibration Method for Event-Driven Structured Light Systems,
  IEEE Sensors Journal, 2020.
• Muglikar, M., Gehrig, M., Gehrig, D., Scaramuzza, D.,
  How to Calibrate Your Event Camera,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2021. Project page, YouTube, Code.
• Huang, K., Wang, Y., Kneip, L.,
  Dynamic Event Camera Calibration,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), 2021. Video, Code.
• Bao, Y., Sun, L., Ma, Y., Gu, D., Wang, K.,
  Improving Fast Auto-Focus with Event Polarity,
  Arxiv, 2023.

• Several event-processing filters in the jAER (java Address-Event Representation) project
• A collection of tracking and detection algorithms using the YARP framework
• Some detection and tracking algorithms in EVL
• Prophesee Open Source library - OpenEB
• Optical Flow
  • LocalPlanesFlow, inspired by the paper Benosman et al., TNNLS 2014.
  • Several algorithms compared in the paper by Rueckauer and Delbruck, FNINS 2016.
  • Event-Lifetime estimation, associated to the paper Mueggler et al., ICRA 2015.
  • EV-FlowNet, associated to the paper Zhu et al., RSS 2018.
• Feature Tracking
  • Event-based Feature Tracking with Probabilistic Data Association, associated to the papers Zhu et al., ICRA 2017 and Zhu et al., CVPR 2017.
  • Tracking code associated to the paper Gehrig et al., IJCV 2019".
  • Evaluation code associated to the paper Gehrig et al., IJCV 2019".
• Intensity-Image reconstruction from events
  • Code for intensity reconstruction, inspired by the paper Kim et al., BMVC 2014.
  • DVS Reconstruction code associated to the paper Reinbacher et al., BMVC 2016.
  • High-pass filter code associated to the paper Scheerlinck et al., ACCV 2018
  • E2VID code associated to the paper Rebecq et al., TPAMI 2020.
  • EVREAL code associated to the paper Ercan et al., CVPRW 2023.
  • HyperE2VID code associated to the paper Ercan et al., arXiv 2023.
• Localization and Ego-Motion Estimation
  • Panoramic tracking code associated to the paper Reinbacher et al., ICCP 2017.
• Pattern Recognition
  • A simple spiking neural network for recognition associated to the paper Orchard et al., TPAMI 2015.

• Process AEDAT: useful scripts to work with data from jAER and cAER.
• Matlab functions in jAER project
• AEDAT Tools: scripts for Matlab and Python to work with aedat files.
• aedat4to2: Python/DV script to convert AEDAT4 from DV to AEDAT2 for jAER.
• aedat4tomat: Python/DV script to convert AEDAT4 from DV to matlab file.
• Matlab AER functions by G. Orchard. Some basic functions for filtering and displaying AER vision data, as well as making videos.
• Python code for AER vision data by G. Orchard.
• edvstools, by D. Weikersdorfer: A collection of tools for the embedded Dynamic Vision Sensor eDVS.
• Tarsier Framework for event-based Vision in C++.
• events_h52bag C++ code to convert event data from HDF5 to ROSbags.
• events_bag2h5 Python code to convert event data from ROSbags to HDF5.
• CelexMatlabToolbox by Yuxin Zhang. Tools to decode events generated by CeleX IV DVS, visualize them and denoise.
• Loris Python package to read files from neuromorphic cameras.
• Marcireau A., Ieng S.-H., Benosman R.,
  Sepia, Tarsier, and Chameleon: A Modular C++ Framework for Event-Based Computer Vision,
  Front. Neurosci. (2020), 13:1338. Code
• BIMVEE Python tools for Batch Import, Manipulation, Visualisation and Export of Events and other timestamped data. Imports from various file formats into a common workspace format, including native Python import of rosbags.
• Tonic provides publicly available event datasets and data transformations much like Torchvision/audio.
• Prophesee automotive dataset toolbox, Code
• dv_ros ROS package for accumulating event frames with iniVation Dynamic Vision System's dv-sdk.
• dvs_event_server ROS package used to transport "dvs/events" ROS topic to Python through protobuf and zmq, because Python ROS callback has a large delay.
• AEStream A fast C++ library with a Python interface for streaming Address Event representations directly from Inivation and Prophesee cameras to various sources, such as STDOUT, UDP (network), or PyTorch.
  
  • Pedersen, J., Conradt, J.,
    AEStream: Accelerated event-based processing with coroutines,
    Proc. Annual Neuro-Inspired Computational Elements Conf. (NICE), 2023, pp. 86-91.
• AEDAT decoder A fast AEDAT 4 Python reader, with a Rust underlying implementation.
• aedat-rs Standalone Rust library for decoding AEDAT 4 files for use in bespoke Rust event systems.
• expelliarmus A pip-installable Python library to decode DAT, EVT2 and EVT3 files generated by Prophesee cameras to structured NumPy arrays.
• ADΔER A suite of tools for transcoding, inspecting, visualizing, and building applications for a unified intensity event representation
  • Freeman, A.,
    The ADΔER Framework: Tools for Event Video Representations,
    ACM Multimedia Systems (MMSys) Doctoral Symposium, 2023. PDF, Code.

• Hoffstaetter, M., Belbachir, N., Bodenstorfer, E., Schoen, P.,
  Multiple Input Digital Arbiter with Timestamp Assignment for Asynchronous Sensor Arrays,
  IEEE Int. Conf. Electronics, Circuits and Systems (ICECS), 2006.
• Belbachir, A., Hofstaetter, M., Reisinger, K., Litzenberger, M., Schoen, P.,
  High-Precision Timestamping and Ultra High-Speed Arbitration of Transient Pixels' Events,
  Int. Conf. on Electronics, Circuits and Systems (ICECS), 2008.
• Hoffstaetter, M., Schoen, P., Posch, C., Bauer, D.,
  An integrated 20-bit 33/5M events/s AER sensor interface with 10ns time-stamping and hardware-accelerated event pre-processing,
  IEEE Biomedical Circuits and Systems Conference (BioCAS), 2009.
• Hoffstaetter, M., Litzenberger, M., Matolin, D., Posch, C.,
  Hardware-accelerated address-event processing for high-speed visual object recognition,
  IEEE Int. Conf. Electronics, Circuits, and Systems (ICECS), 2011.
• Dynamic Neuromorphic Asynchronous Processor (DYNAP) by aiCTX AG
  • Qiao, N., Mostafa, H., Corradi, F., Osswald, M., Stefanini, F., Sumislawska, D., Indiveri, G.,
    A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses,
    Front. Neurosci. (2015), 9:141. PDF
  • Indiveri, G., Qiao, N., Corradi, F.,
    Neuromorphic Architectures for Spiking Deep Neural Networks,
    IEEE Int. Electron Devices Meeting (IEDM), 2015. PDF
• Wiesmann, G., Schraml, S., Litzenberger, M., Belbachir, A. N., Hofstatter, M., Bartolozzi, C.,
  Event-driven embodied system for feature extraction and object recognition in robotic applications,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012.
• Galluppi, F., Denk, C., Meiner, M. C., Stewart, T. C., Plana, L. A., Eliasmith, C., Furber, S., Conradt, J.,
  Event-based neural computing on an autonomous mobile platform,
  IEEE Int. Conf. Robotics and Automation (ICRA), 2014. PDF
• Graf, R., King, R., Belbachir, A.,
  Braille Vision Using Braille Display and Bio-inspired Camera,
  Int. Conf. Computer Supported Education (CSEDU), SCITEPRESS Digital Library, (2014), pp. 214 - 219.
• Sengupta, J. P., Villemur, M., Mendat, D. R., Tognetti, G., Andreou, A. G.,
  Architecture and Algorithm Co-Design Framework for Embedded Processors in Event-Based Cameras,
  IEEE Int. Symp. Circuits and Systems (ISCAS), 2020, pp. 1-5.
• Gao, Y., Wang, S., So, H. K. H.,
  REMOT: A Hardware-Software Architecture for Attention-Guided Multi-Object Tracking with Dynamic Vision Sensors on FPGAs,
  ACM/SIGDA Int. Symp. Field-Programmable Gate Arrays (FPGA), 2022. Code

• Event-based Robot Vision at TU Berlin.
  • YouTube videos and Slides with links
• Projects course: Bio-inspired Computer (Event-based) Vision at TU Berlin

• Mahowald, M.,
  VLSI Analogs of Neuronal Visual Processing: A Synthesis of Form and Function,
  Ph.D. thesis, California Inst. Of Technology, Pasadena, CA, 1992. PDF
  She won the Caltech's Clauser prize for the best PhD thesis for this work, which included the silicon retina, AER communication, and a beautiful stereopsis chip.
  • Kluwer book from Misha’s thesis: Mahowald, M., An Analog VLSI System for Stereoscopic Vision. Boston: Springer Science & Business Media, 1994.
• Delbrück, T.,
  Investigations of Analog VLSI Visual Transduction and Motion Processing,
  Ph.D. Thesis. California Inst. Of Technology, Pasadena, CA, 1993. PDF
• Lichtsteiner, P.,
  A temporal contrast vision sensor,
  Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2006. PDF
• Matolin, D.,
  Asynchronous CMOS image sensor with extended dynamic range and suppression of time-redundant data,
  Ph.D. Thesis, TU Dresden & AIT, deutsch, 2010.
• Berner, R.,
  Building Blocks for Event-Based Sensors,
  Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2011. PDF
• Ni, Z.,
  Asynchronous Event Based Vision: Algorithms and Applications to Microrobotics,
  Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2013.
• Carneiro, J.,
  Asynchronous Event-Based 3D Vision,
  Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2014.
• Weikersdorfer, D.,
  Efficiency by Sparsity: Depth-Adaptive Superpixels and Event-based SLAM,
  Ph.D. Thesis, Technical University of Munich, Munich, Germany, 2014. PDF
• Borer, D. J.,
  4D Flow Visualization with Dynamic Vision Sensors,
  Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2014. PDF
• Yang, M.,
  Silicon Retina and Cochlea with Asynchronous Delta Modulator for Spike Encoding,
  Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2015.
• Brändli, C.,
  Event-Based Machine Vision,
  Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2015. PDF
• Lagorce, X.,
  Computational methods for event-based signals and applications,
  Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2015. PDF
• Kogler, J.,
  Design and evaluation of stereo matching techniques for silicon retina cameras,
  Ph.D. Thesis, Technische Universität Wien, Vienna, Austria, 2016. PDF
• Moeys, D. P.,
  Analog and digital implementations of retinal processing for robot navigation systems,
  Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2016. PDF
• Cohen, G. K.,
  Event-Based Feature Detection, Recognition and Classification,
  Ph.D. Thesis, Université de Pierre et Marie Curie, Paris, France, 2016. PDF
• Li, C.,
  Two-stream vision sensors,
  Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2017.
• Neil, D.,
  Deep Neural Networks and Hardware Systems for Event-driven Data,
  Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2017. PDF
• Mueggler, E.,
  Event-based Vision for High-Speed Robotics,
  Ph.D. Thesis, University of Zurich, Zurich, Switzerland, 2017.
• Kim, H.,
  Real-time visual SLAM with an event camera,
  Ph.D. Thesis, Imperial College London, United Kingdom, 2017.
• Huang, J.,
  Asynchronous high-speed feature extraction image sensor (CelePixel),
  Ph.D. Thesis, Nanyang Technological University, Singapore, 2018.
• Gibson, T. T.,
  Inspired by nature: timescale-free and grid-free event-based computing with spiking neural networks,
  Ph.D. Thesis, The University of Queensland, Brisbane, Australia, 2018.
• Everding, L.,
  Event-Based Depth Reconstruction Using Stereo Dynamic Vision Sensors,
  Ph.D. Thesis, Technical University of Munich, Munich, Germany, 2018.
• Seifozzakerini, S.,
  Analysis of object and its motion in event-based videos,
  Ph.D. Thesis, Nanyang Technological University, Singapore, 2018.
• Martel, J.,
  Unconventional Processing with Unconventional Visual Sensing. Parallel, Distributed and Event Based Vision Algorithms & Systems,
  Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2019.
• Bardow, P. A.,
  Estimating General Motion and Intensity from Event Cameras,
  Ph.D. Thesis, Imperial College London, United Kingdom, 2019.
• Ye, C.,
  Learning of Dense Optical Flow, Motion and Depth, from Sparse Event Cameras,
  Ph.D. Thesis, University of Maryland, USA, 2019.
• Liu, H.,
  Neuromorphic Vision for Robotic Tracking and Navigation,
  Ph.D. Thesis, ETH-Zurich, Zurich, Switzerland, 2019.
• Zhu, A. Z.,
  Event-Based Algorithms for Geometric Computer Vision,
  Ph.D. Thesis, University of Pennsylvania, USA, 2019.
• Rebecq, H.,
  Event Cameras: from SLAM to High Speed Video,
  Ph.D. Thesis, University of Zurich, Zurich, Switzerland, 2019.
• Kaiser, J.,
  Synaptic Learning for Neuromorphic Vision,
  Ph.D. Thesis, Karlsruher Instituts für Technologie (KIT), Karlsruhe, Germany, 2020.
• Wang, Z. (Winston),
  Synergy of physics and learning-based models in computational imaging and display,
  Ph.D. Thesis, Northwestern University, 2020. YouTube.
• Mitrokhin, A.,
  Motion Segmentation and Egomotion Estimation with Event-Based Cameras,
  Ph.D. Thesis, University of Maryland, USA, 2020.
• Scheerlinck, C.,
  How to See with an Event Camera,
  Ph.D. Thesis, Australian National University, Canberra, Australia, 2021. PDF
• Stoffregen, T.,
  Motion Estimation by Focus Optimisation: Optic Flow and Motion Segmentation with Event Cameras,
  Ph.D. Thesis, Monash University, Melbourne, Australia, 2021.
• Monforte, M.,
  Trajectory Prediction with Event-Based Cameras for Robotics Applications,
  Ph.D. Thesis, Italian Institute of Technology, Genoa, Italy, 2021. PDF
• Lenz, G.,
  Neuromorphic algorithms and hardware for event-based processing,
  Ph.D. Thesis, Sorbonne University, Paris, France, 2021. PDF
• Alzugaray, I.,
  Event-driven Feature Detection and Tracking for Visual SLAM,
  Ph.D. Thesis, ETH Zurich, Zurich, Switzerland, 2022. PDF
• Liu, D.,
  Motion Estimation Using an Event Camera,
  Ph.D. Thesis, University of Adelaide, Adelaide, Australia, 2022.
• See also Theses from Delbruck's group at INI
• Chamorro, W. O.,
  Event-based Simultaneous Localization and Mapping,
  Ph.D. Thesis, Universitat Politècnica de Catalunya, Barcelona, Spain, 2023. PDF

• Reisinger, K.,
  EMC testing on Silicon Retinas,
  MSc. Thesis, TU Wien & AIT, Austria, 2006.
• Nowakowska, A.,
  Recognition of a vision approach for fall detection using a biologically inspired dynamic stereo vision sensor,
  MSc. Thesis, TU Wien & AIT, Austria, 2011.
• Reingruber, H.,
  An Asynchronous Data Interface for Event-based Stereo Matching,
  MSc. Thesis, TU Wien & AIT, Austria, 2011.
• Zima, M.,
  Hand/Arm Gesture Recognition based on Address-Event-Representation Data,
  MSc. Thesis, TU Wien & AIT, Austria, 2012.
• Huber, B.,
  High-Speed Pose Estimation using a Dynamic Vision Sensor,
  MSc. Thesis, University of Zurich, Switzerland, 2014.
• Horstschaefer, T.,
  Parallel Tracking, Depth Estimation, and Image Reconstruction with an Event Camera,
  MSc. Thesis, University of Zurich, Switzerland, 2016.
• Kaelber, F., (Everding, L., Conradt, J.,)
  A probabilistic method for event stream registration,
  Bacherlor Thesis, TU Munich, Germany, 2016.
• Galanis, M., (Everding, L., Conradt, J.,)
  DVS event stream registration,
  Bacherlor Thesis, TU Munich, Germany, 2016.
• Paredes-Valles, F.,
  Neuromorphic Computing of Event-Based Data for High-Speed Vision-Based Navigation,
  MSc. Thesis, TU Delft, The Netherlands, 2018.
• Nelson, K. J.,
  Event-Based Visual-Inertial Odometry on a Fixed-Wing Unmanned Aerial Vehicle,
  MSc. Thesis, Air Force Institute of Technology, USA, 2019. PDF, PDF
• Attanasio, G.,
  Event-based camera communications: a measurement-based analysis,
  MSc. Thesis, Politecnico di Torino, Italy, 2019.
• Wang, Z.,
  Motion Equivariance of Event-based Camera Data with the Temporal Normalization Transform,
  MSc. Thesis, University of Pennsylvania, USA, 2019.
• Boettiger, J. P.,
  A Comparative Evaluation of the Detection and Tracking Capability Between Novel Event-Based and Conventional Frame-Based Sensors,
  MSc. Thesis, Air Force Institute of Technology, USA, 2020. PDF
• Friedel, Z. P.,
  Event-Based Visual-Inertial Odometry Using Smart Features,
  MSc. Thesis, Air Force Institute of Technology, USA, 2020.
• Verecken, J.,
  Embedded real-time inference in spiking neural networks for neuromorphic IoT vision sensors,
  MSc. Thesis, Ecole polytechnique de Louvain, Université catholique de Louvain, Belgium, 2020.
• Gava, L.,
  Event-driven Motion-In-Depth for Low Latency Control of the Humanoid Robot iCub,
  MSc. Thesis, University of Genoa, Italy, 2020.
• Dubeau, E.,
  Suivi d'objet en 6 degrés de liberté avec caméra événementielle (Object Tracking in 6-DOF using an event camera),
  MSc. Thesis, Université Laval, Canada, 2022.

• Institute of NeuroInformatics (INI) of the University of Zurich (UZH) and ETH Zurich, Switzerland.
  • INI Sensors Group Videos
• iniVation AG (commercialization of neuromorphic vision technology from INI), Switzerland.
• Dynamic Vision Sensor (DVS) - asynchronous temporal contrast silicon retina
• Western Sydney University’s International Centre for Neuromorphic Systems (ICNS), Australia.
• GRASP Lab at University of Pennsylvania, Kostas Daniilidis.
• Robotics and Perception Group of the University of Zurich (UZH) and ETH Zurich, Switzerland.
• Robotic Interactive Perception of TU Berlin, Germany.
• Perception and Robotics Group at University of Maryland (UMD). Fermüller's Lab on Event-based vision
• Event-Driven Perception for Robotics (EDPR) group at Istituto Italiano di Tecnologia (IIT), Italy.
• Computer Vision and Robotics Lab of University of Granada, Spain.
• Institut de la Vision Neuromorphics group Paris, France.
• AIT Austrian Institute of Technology Sensing & vision solutions group in Vienna, Austria.
• Sinapse Singapore Institute for Neurotechnology, Singapore.
• Intel Labs, Mike Davies (Intel’s neuromorphic computing program leader).
  • Video CVPRW 2019
  • Video Intel NICE 2018 Loihi, Video Intel NICE 2018 Day 3
  • Advancing neuromorphic computing from lab to mainstream applications -Telluride 2020
• Robotics and Technology of Computers Lab - Sevilla (RTC) of the University of Seville (USE), Seville, Spain.
• IMSE-CNM – Seville Institute of Microelectronics, Seville, Spain. News
• Prophesee SA: Sensor and Software development and production.
• Adaptive Robotic Controls Lab (ArcLab) of the University of Hong Kong (HKU).
• Neuromorphic Automation and Intelligence Lab (NAIL) at the Hunan University, China.
• Mobile Perception Lab (MPL) at ShanghaiTech University, China.
• Camera Intelligence Lab at Peking University (PKU), China.
• Nano(neuro)electronics Research Laboratory at Purdue University, USA.
• Martinet Lab at Université Côte d'Azur, France.
• Hyper Vision Research Laboratory at Keio University, Japan.
• Aoki Media Sensing Lab at Keio University, Japan.
• Visual Intelligence Lab. at KAIST, South Korea.
• Neuromorphic Computing Group (NCG) at ZHAW Zurich University of Applied Sciences, Switzerland.
• NeuroPAC

• Neuromorphic Revolution to Start in 2024, 10.2019.
• Neuromorphic Vision Sensors Eye the Future of Autonomy, 04.2020.
• Neuromorphic Vision Sensors Bring Autonomy Closer to Reality, 04.2020.
• The Slow But Steady Rise of the Event Camera, 06.2020
• Europe Still the Focal Point for Neuromorphic Vision, 07.2020.
• Telluride Neuromorphic Engineering Workshop Goes Large, 07.2020.
• Prophesee Touts Toolkit for Event-based Vision, 09.2020.
• Neuromorphic Vision in Space, 07.2021.
• Prophesee Showcases Neuromorphic Vision Systems from Biotech to Space Debris, 12.2021.
• What Does “Neuromorphic” Mean Today?, EETimes Special Issue, 07.2022.
  • Exclusive: An Interview with Carver Mead, 07.2022.
  • Inspiration or Imitation: How Closely Should We Copy Biological Systems?, 07.2022.
  • A Shift in Computer Vision is Coming, 04.2022.
  • Neuromorphic Sensing: Coming Soon to Consumer Products, 07.2022.
  • Reverse-Engineering Insect Brains to Make Robots, 07.2022.
  • Cars That Think Like You, 07.2022.
• Embedded AI Processors: The Cambrian Explosion
• EU-Funded NimbleAI to Deliver 3D Neuromorphic Chip, 04.2023.
• Brains and Machine Podcast, since 09.2023. A podcast about neuromorphic engineering and bio-inspired technology.

• Event-Based Vision: Taking a Cue From Biology, 03.2021.
• Silicon retinas to help robots navigate the world, Advanced Science News, 10.2022.
• Event cameras at TU Berlin - SCIoI, 06.2023.

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