README.md

Event-based Vision Resources

Table of Contents:

• Devices and Manufacturers

• Companies working on Event-based Vision

• Neuromorphic Systems

• Algorithms

  • Feature Detection and Tracking
  • Depth Estimation (3D Reconstruction)
    • Monocular
    • Stereo
  • Optical Flow Estimation
  • Intensity-Image Reconstruction
  • Localization and Ego-motion estimation
  • Visual Odometry and SLAM (Simultaneous Localization And Mapping)
  • Visual-Inertial Odometry
  • Visual Stabilization
  • Video Processing
  • Pattern recognition
  • Control

• Datasets and Simulators

• Software

  • Drivers
  • Calibration
  • Algorithms
  • Utilities

• Neuromorphic Processors and Platforms

• Workshops and Tutorials

• Theses and Dissertations

• People / Organizations

• Contributing

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Devices & Companies Manufacturing them

• 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.
  • Product page at iniLabs. Buy a DVS
  • Introductory videos about the DVS
  • iniLabs invents, produces and sells neuromorphic technologies for research.
• 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.
  • Product page at iniLabs. Buy a DAVIS
  • 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), Lisbon, 2015, pp. 718-721.
• 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.
  • Chronocam
• 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.
• Samsung's DVS (Gen2)
  • Son, B., et al., 4.1 A 640×480 dynamic vision sensor with a 9µm pixel and 300Meps address-event representation, IEEE Int. Solid-State Circuits Conf. (ISSCC), San Francisco, CA, 2017, pp. 66-67.
  • Slides and Video by Yoel Yaffe, Samsung Israel Research Center, Samsung Electronics.
• CeleX (Hillhouse Technology, Singapore). YouTube
• Insightness AG. The Silicon Eye Technology
  • Slides and Video by Christian Brandli, CEO and co-founder of Insightness.
• iniVation invents, produces and sells neuromorphic technologies with a special focus on event-based vision into business.
  • Slides by S. E. Jakobsen, CEO and co-founder of iniVation.

Companies working on Event-based Vision

• iniLabs invents, produces and sells 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).
• Chronocam develops bio-inspired and self-adapting approach to the need for visual sensing and processing in autonomous vehicles, connected devices, security and surveillance systems.
• 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.
• iniVation invents, produces and sells neuromorphic technologies with a special focus on event-based vision into business.
• 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).

Neuromorphic Systems

• Delbruck, T.,
  Frame-free dynamic digital vision*,
  Int. Symp. Secure-Life Electronics, Advanced Electronics for Quality Life and Society, University of Tokyo, Tokyo, Japan, Mar. 6-7, 2008, pp. 21-26. Introduces the software architecture of jAER and shows examples of several event-based processing algorithms.
• Liu, S.-C. and Delbruck, T.,
  Neuromorphic sensory systems,
  Current Opinion in Neurobiology, 20:3(288-295), 2010.
• 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.
• 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.
• Delbruck, T.,
  Neuromorophic Vision Sensing and Processing (Invited paper),
  46th Eur. Solid-State Device Research Conference (ESSDERC), Lausanne, 2016, pp. 7-14.
• Vanarse, A., Osseiran, A., Rassau, A,
  A Review of Current Neuromorphic Approaches for Vision, Auditory, and Olfactory Sensors, Front. Neurosci. (2016), 10:115.

Algorithms

Feature Detection and Tracking

• 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.
• 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., Toronto, Ont., 2006, pp. 653-658. PDF
• Drazen, D., Lichtsteiner, P., Haefliger, P., Delbruck, T., Jensen, A.,
  Toward real-time particle tracking using an event-based dynamic vision sensor,
  Experiments in Fluids (2011), 51(1):1465-1469. PDF
• Ni, Z., Pacoret, Benosman, R., Ieng, S., Reginer, S.,
  Asynchronous event-based high speed vision for microparticle tracking,
  J. Microscopy (2011), 245(1):236-244.
• 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. (2012), 28(5):1081-1089.
• 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), Providence, RI, 2012, pp. 35-40. PDF
• Ni, Ph.D. Thesis, 2013,
  Asynchronous Event Based Vision: Algorithms and Applications to Microrobotics.
• Borer, D., Rosgen, T.,
  Large-scale Particle Tracking with Dynamic Vision Sensors,
  ISFV16 - 16th Int. Symp. Flow Visualization, Okinawa 2014. Project page, PDF
• 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), Lausanne, 2014, pp. 178-178.
• 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. (2015), 26(8):1710-1720.
• Clady, X., Ieng, S.-H., Benosman, R.,
  Asynchronous event-based corner detection and matching,
  Neural Networks (2015), 66:91-106.
• 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.
• 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), Lisbon, 2015, pp. 2417-2420.
• Barranco, F., Teo, C. L., Fermüller, C., Aloimonos, Y.,
  Contour Detection and Characterization for Asynchronous Event Sensors,
  IEEE Int. Conf. Computer Vision (ICCV), 2015, Santiago, Chile, pp. 486-494. PDF
• 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), Montreal, QC, 2016, pp. 2511-2514.
• 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), Krakow, Poland, 2016. PDF, YouTube
• 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), Krakow, Poland, 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), Daejeon, South Korea, 2016, pp. 2203-2208. YouTube, Code
• Vasco, V., Glover, A., Bartolozzi, C.,
  Fast event-based Harris corner detection exploiting the advantages of event-driven cameras,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016, pp. 4144-4149. YouTube, Code
• Clady, X., Maro, J.-M., Barré, S., Benosman, R. B.,
  A Motion-Based Feature for Event-Based Pattern Recognition.
  Front. Neurosci. (2017), 10:594.
• Zhu, A., Atanasov, N., Daniilidis, K.,
  Event-based Feature Tracking with Probabilistic Data Associations,
  IEEE Int. Conf. Robotics and Automation (ICRA), Singapore, 2017. YouTube
• Mueggler, E., Bartolozzi, C., Scaramuzza, D.,
  Fast Event-based Corner Detection,
  British Machine Vision Conf. (BMVC), London, 2017. PDF, YouTube
• Glover, A. and Bartolozzi, C.,
  Robust Visual Tracking with a Freely-moving Event Camera,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. YouTube, Code
• Barrios-Avilés, J., Iakymchuk, T., Samaniego, J., Rosado-Muñoz, A.,
  An Event-based Fast Movement Detection Algorithm for a Positioning Robot Using POWERLINK Communication,
  arXiv 1707.07188.
• Li, J., Shi, F., Liu, W., Zou, D., Wang, Q., Park, Paul-K.J., Hyunsurk, E.R.,
  Adaptive Temporal Pooling for Object Detection using Dynamic Vision Sensor,
  British Machine Vision Conf. (BMVC), London, 2017. PDF

Depth Estimation (3D Reconstruction)

Monocular Depth Estimation

• Rebecq, H., Gallego, G., Scaramuzza, D.,
  EMVS: Event-based Multi-View Stereo,
  British Machine Vision Conf. (BMVC), York, 2016. PDF, YouTube, 3D Reconstruction Experiments from a Train using an Event Camera
• Kim et. al. ECCV 2016,
  Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera.

Monocular Depth Estimation using Structured Light

• Brandli, C., Mantel, T.A., Hutter, M., Hoepflinger, M.A., Berner, R., Siegwart, R., Delbruck, T.,
  Adaptive Pulsed Laser Line Extraction for Terrain Reconstruction using a Dynamic Vision Sensor,
  Front. Neurosci. (2014) 7:275. PDF, YouTube
• Matsuda, N., Cossairt, O., Gupta, M.,
  MC3D: Motion Contrast 3D Scanning,
  IEEE Conf. Computational Photography (ICCP), Houston,TX, 2015, pp. 1-10. PDF, YouTube, Project page

Stereo Depth Estimation

• Schraml, C., Schon, P., Milosevic, N.,
  Smartcam for real-time stereo vision - address-event based embedded system,
  Int. Conf. Computer Vision Theory and Applications (VISAPP), Barcelona, Spain, 2007, pp. 466-471.
• Kogler, J., Sulzbachner, C., Kubinger, W.,
  Bio-inspired stereo vision system with silicon retina imagers,
  Int. Conf. Computer Vision Systems (ICVS), 2009, pp. 174-183. PDF
• Schraml, S., Belbachir, A. N., Milosevic, N., Schon, P.,
  Dynamic stereo vision system for real-time tracking,
  IEEE Int. Symp. Circuits and Systems (ISCAS), Paris, 2010, pp. 1409-1412.
• Kogler, J., Sulzbachner, C., Humenberger, M., Eibensteiner, F.,
  Address-Event Based Stereo Vision with Bio-Inspired Silicon Retina Imagers,
  Advances in Theory and Applications of Stereo Vision (2011), pp. 165-188.
• Kogler, J., Humenberger, M., Sulzbachner, C.,
  Event-Based Stereo Matching Approaches for Frameless Address Event Stereo Data,
  Int. Symp. Visual Computing (ISVC) 2011, Advances in Visual Computing, pp. 674-685.
• Benosman, R., Ieng, S. H., Rogister, P., Posch, C.,
  Asynchronous Event-Based Hebbian Epipolar Geometry,
  IEEE Trans. Neural Netw. (2011), 22(11):1723-1734.
• Lee et. al., ISCAS 2012
• Rogister, P. , Benosman, R., Ieng, S.-H., Lichtsteiner, P., Delbruck, T.,
  Asynchronous Event-Based Binocular Stereo Matching,
  IEEE Trans. Neural Netw. Learn. Syst., 23(2):347-353, 2012.
• Carneiro, J., Ieng, S.-H., Posch, C., Benosman, R.,
  Event-based 3D reconstruction from neuromorphic retinas,
  Neural Networks (2013), 45:27-38.
• Carneiro, Ph.D. Thesis, 2014,
  Asynchronous Event-Based 3D Vision.
• Piatkowska, E., Belbachir, A. N., Gelautz, M.,
  Asynchronous Stereo Vision for Event-Driven Dynamic Stereo Sensor Using an Adaptive Cooperative Approach,
  IEEE Int. Conf. Computer Vision Workshops (ICCVW), Sydney, NSW, 2013, pp. 45-50.
• Piatkowska, E., Belbachir, A. N., Gelautz, M.,
  Cooperative and asynchronous stereo vision for dynamic vision sensors,
  Meas. Sci. Technol. (2014), 25(5).
• Lee, J. H., Delbruck, T., Pfeiffer, M., Park, P. K. J., Shin, C.-W., Ryu, H., Kang, B. C., Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas,
  IEEE Trans. Neural Netw. Learn. Syst. (2014), 25(2):2250-2263.
• Camuñas-Mesa, L. A., Serrano-Gotarredona, T., Ieng, S. H., Benosman, R. B., Linares-Barranco, B.,
  On the use of orientation filters for 3D reconstruction in event–driven stereo vision,
  Front. Neurosci. (2014) 8:48.
• Camuñas-Mesa, L. A., Serrano-Gotarredona, T., Linares-Barranco, B., Ieng, S., Benosman, R.,
  Event-Driven Stereo Vision with Orientation Filters,
  IEEE Int. Symp. Circuits and Systems (ISCAS), Melbourne VIC, 2014, pp. 257-260.
• Belbachir, A. N., Schraml, S., Mayerhofer, M., Hofstatter, M.,
  A Novel HDR Depth Camera for Real-time 3D 360-degree Panoramic Vision,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2014, pp. 419-426. PDF
• Eibensteiner, F., Kogler, J., Scharinger, J.,
  A High-Performance Hardware Architecture for a Frameless Stereo Vision Algorithm Implemented on a FPGA Platform,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Columbus, OH, 2014, pp. 637-644.
• Schraml, S., Belbachir, A. N., Bischof, H.,
  Event-Driven Stereo Matching for Real-Time 3D Panoramic Vision,
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 466-474. PDF. Slides.
• S. Schraml, A. N. Belbachir, Bischof, H.,
  An Event-Driven Stereo System for Real-Time 3-D 360° Panoramic Vision,
  IEEE Trans. Ind. Electron. (2016), 63(1):418-428.
• Firouzi, M. and Conradt, J.,
  Asynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas,
  Neural Processing Letters, 2016, 43(2):311-326. PDF
• Osswald, M., Ieng, S.-H., Benosman, R., Indiveri, G.,
  A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems,
  Scientific Reports 7, Article number: 40703 (2017).
• Piatkowska, E., Kogler, J., Belbachir, N., Gelautz, M.,
  Improved Cooperative Stereo Matching for Dynamic Vision Sensors with Ground Truth Evaluation,
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA, 2017, pp. 370-377. PDF.
• Dikov, G., Firouzi, M., Röhrbein, F., Conradt, J., Richter, C.,
  Spiking Cooperative Stereo-Matching at 2 ms Latency with Neuromorphic Hardware,
  Conf. Biomimetic and Biohybrid Systems. Living Machines 2017: Biomimetic and Biohybrid Systems, pp. 119-137. Lecture Notes in Computer Science, vol 10384. Springer, Cham. PDF, Videos
• Eibensteiner, F., Brachtendorf, H. G., Scharinger, J.,
  Event-driven stereo vision algorithm based on silicon retina sensors,
  27th Int. Conf. Radioelektronika, Brno, 2017, pp. 1-6.
• Zou, D., Shi, F., Liu, W., Li, J., Wang, Q., Park P.-K.J., Hyunsurk, E. R.,
  Robust Dense Depth Maps Generations from Sparse DVS Stereos,
  British Machine Vision Conf. (BMVC), London, 2017. PDF, Supp. Material.

Optical Flow Estimation

• Cook et. al. IJCNN 2011,
  Interacting maps for fast visual interpretation.
  Joint estimation of optical flow, image intensity and angular velocity with a rotating event camera.
• Benosman, R., Ieng, S.-H., Clercq, C., Bartolozzi, C., Srinivasan, M.,
  Asynchronous Frameless Event-Based Optical Flow,
  Neural Networks (2012), 27:32-37.
• Benosman, R., Clercq, C., Lagorce, X., Ieng, S.-H., Bartolozzi, C.,
  Event-Based Visual Flow,
  IEEE Trans. Neural Netw. Learn. Syst. (2014), 25(2):407-417.
  • Code (jAER): LocalPlanesFlow
• Orchard, G., Benosman, R., Etienne-Cummings, R., Thakor, N,
  A Spiking Neural Network Architecture for Visual Motion Estimation,
  IEEE Biomedical Circuits and Systems Conf. (BioCAS), Rotterdam, 2013, pp. 298-301.
• Clady, X., Clercq, C., Ieng, S.H., Houseini, F., Randazzo, M., Natale, L., Bartolozzi, C., Benosman, R.,
  Asynchronous visual event-based time-to-contact,
  Front. Neurosci. (2014), 8:9.
• Tschechne, S., Sailer R., Neumann, H.,
  Bio-Inspired Optic Flow from Event-Based Neuromorphic Sensor Input,
  IAPR Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR) 2014, pp. 171-182.
• Barranco, F., Fermüller, C., Aloimonos, Y.,
  Contour motion estimation for asynchronous event-driven cameras,
  Proc. IEEE (2014), 102(10):1537-1556. PDF
• Barranco, F., Fermüller, C., Aloimonos, Y.,
  Bio-inspired Motion Estimation with Event-Driven Sensors,
  Int. Work-Conf. Artificial Neural Networks (IWANN) 2015, Advances in Computational Intelligence, pp. 309-321.
• Conradt, J.,
  On-Board Real-Time Optic-Flow for Miniature Event-Based Vision Sensors,
  IEEE Int. Conf. Robotics and Biomimetics (ROBIO), Zhuhai, China, 2015, pp. 1858-1863.
• Brosch, T., Tschechne, S., Neumann, H.,
  On event-based optical flow detection,
  Front. Neurosci. (2015), 9:137.
• Kosiorek, A., Adrian, D., Rausch, J., Conradt, J.,
  An Efficient Event-Based Optical Flow Implementation in C/C++ and CUDA,
  Tech. Rep. TU Munich, 2015.
• E. Mueggler, C. Forster, N. Baumli, G. Gallego, D. Scaramuzza,
  Lifetime Estimation of Events from Dynamic Vision Sensors,
  IEEE Int. Conf. Robotics and Automation (ICRA), Seattle (WA), USA, 2015, pp. 4874-4881. PDF, Code
• Rueckauer, B. and Delbruck, T.,
  Evaluation of Event-Based Algorithms for Optical Flow with Ground-Truth from Inertial Measurement Sensor,
  Front. Neurosci (2016). 10:176.
  • Code (jAER)
• Bardow, P. A., Davison, A. J., Leutenegger, S.,
  Simultaneous Optical Flow and Intensity Estimation from an Event Camera,
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016. YouTube
• Liu, M., Delbruck, T.,
  Block-Matching Optical Flow for Dynamic Vision Sensors: Algorithm and FPGA Implementation,
  IEEE Int. Symp. Circuits and Systems (ISCAS), Baltimore, MD, 2017.

Intensity-Image Reconstruction from events

• Cook, M., Gugelmann, L., Jug, F., Krautz, C., Steger, A.,
  Interacting maps for fast visual interpretation,
  Int. Joint Conf. on Neural Networks (IJCNN), San Jose, CA, 2011, pp. 770-776. YouTube
  • Martel, J. N. P., Cook, M.,
    A Framework of Relational Networks to Build Systems with Sensors able to Perform the Joint Approximate Inference of Quantities,
    IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Workshop on Unconventional Computing for Bayesian Inference, 2015, Hamburg. PDF
  • Martel, J. N. P., Chau, M., Dudek, P., Cook, M.,
    Toward joint approximate inference of visual quantities on cellular processor arrays,
    IEEE Int. Symp. Circuits and Systems (ISCAS), Lisbon, 2015, pp. 2061-2064.
• Kim, H., Handa, A., Benosman, R., Ieng, S.-H., Davison, A. J.,
  Simultaneous Mosaicing and Tracking with an Event Camera, British Machine Vision Conference, 2014. PDF, YouTube.
  • Code for intensity reconstruction.
• Barua, S., Miyatani, Y., Veeraraghavan, A.,
  Direct face detection and video reconstruction from event cameras,
  IEEE Winter Conf. Applications of Computer Vision (WACV), Lake Placid, NY, 2016, pp. 1-9. YouTube
• Bardow et. al. CVPR 2016,
  Simultaneous Optical Flow and Intensity Estimation from an Event Camera.
• Reinbacher, C., Graber, G., Pock, T.,
  Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation,
  British Machine Vision Conf. (BMVC), York, 2016. PDF, YouTube, Code
• Moeys, D. P., Li, C., Martel, J. N. P., Bamford, S., Longinotti, L., Motsnyi, V., Bello, D. S. S., Delbruck, T.,
  Color Temporal Contrast Sensitivity in Dynamic Vision Sensors,
  IEEE Int. Symp. Circuits and Systems (ISCAS), Baltimore, MD, 2017. PDF.

Localization and Ego-Motion Estimation

• Cook et. al. IJCNN 2011,
  Interacting maps for fast visual interpretation.
  Joint estimation of optical flow, image intensity and angular velocity with a rotating event camera.
• Weikersdorfer, D. and Conradt, J.,
  Event-based particle filtering for robot self-localization,
  IEEE Int. Conf. on Robotics and Biomimetcs (ROBIO), Guangzhou, 2012, pp. 866-870. PDF
• Censi, A., Strubel, J., Brandli, C., Delbruck, T., Scaramuzza, D.,
  Low-latency localization by Active LED Markers tracking using a Dynamic Vision Sensor,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Tokyo, 2013. PDF, Slides
• Mueggler, E., Huber, B., Scaramuzza, D.,
  Event-based, 6-DOF Pose Tracking for High-Speed Maneuvers,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Chicago, IL, 2014, pp. 2761-2768. PDF, YouTube
• Gallego, G., Forster, C., Mueggler, E., Scaramuzza, D.,
  Event-based Camera Pose Tracking using a Generative Event Model,
  arXiv:1510.01972, 2015.
• Mueggler, E., Gallego G., Scaramuzza, D.,
  Continuous-Time Trajectory Estimation for Event-based Vision Sensors,
  Robotics: Science and Systems XI (RSS), Rome, Italy, 2015. [PDF]
• Gallego, G., Lund, J.E.A., Mueggler, E., Rebecq, H., Delbruck, T., Scaramuzza, D.,
  Event-based, 6-DOF Camera Tracking for High-Speed Applications,
  (Under review), 2016. YouTube
• Reinbacher, C., Munda, G., Pock, T.,
  Real-Time Panoramic Tracking for Event Cameras,
  IEEE Int. Conf. Computational Photography (ICCP), Stanford, CA, USA, 2017, pp. 1-9. PDF, YouTube, Code
• Mueggler et. al. IJRR 2017.
  The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.
• Vasco, V., Glover, A., Mueggler, E., Scaramuzza, D., Natale, L., Bartolozzi, C.
  Independent Motion Detection with Event-driven Cameras,
  Int. Conf. Advanced Robotics (ICAR), Hong Kong, China, 2017, pp. 530-536. PDF
• Nguyen, A., Do, T.-T., Caldwell, D. G., Tsagarakis, N. G.,
  Real-Time Pose Estimation for Event Cameras with Stacked Spatial LSTM Networks,
  arXiv 1708.09011.

Visual Odometry and SLAM (Simultaneous Localization And Mapping)

• Weikersdorfer, D., Hoffmann, R., Conradt. J.,
  Simultaneous localization and mapping for event-based vision systems.
  Int. Conf. Computer Vision Systems (ICVS), 2013, pp. 133-142. PDF, Slides
• Censi, A. and Scaramuzza, D.,
  Low-latency Event-based Visual Odometry,
  IEEE Int. Conf. Robotics and Automation (ICRA), Hong-Kong, 2014, pp. 703-710. PDF, Slides
• Weikersdorfer, D., Adrian, D. B., Cremers, D., Conradt, J.,
  Event-based 3D SLAM with a depth-augmented dynamic vision sensor,
  IEEE Int. Conf. Robotics and Automation (ICRA), Hong-Kong, 2014, pp. 359-364.
• Weikersdorfer, Ph.D. Thesis, 2014,
  Efficiency by Sparsity: Depth-Adaptive Superpixels and Event-based SLAM.
• Kueng, B., Mueggler, E., Gallego, G., Scaramuzza, D.,
  Low-Latency Visual Odometry using Event-based Feature Tracks,
  IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Daejeon, South Korea, 2016, pp. 16-23. PDF. YouTube
• Kim, H., Leutenegger, S., Davison, A.J.,
  Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera,
  European Conference on Computer Vision (ECCV), 2016, pp. 349-364. PDF, YouTube
• Rebecq, H., Horstschaefer, T., Gallego, G., Scaramuzza, D.,
  EVO: A Geometric Approach to Event-based 6-DOF Parallel Tracking and Mapping in Real-time,
  IEEE Robotics and Automation Letters (RA-L), 2:2(593-600), 2017. PDF, Youtube.
• Gallego, G. and Scaramuzza, D.,
  Accurate Angular Velocity Estimation with an Event Camera,
  IEEE Robotics and Automation Letters (RA-L), 2:2(632-639), 2017. PDF, Youtube.
• Mueggler et. al. IJRR 2017.
  The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.

Visual-Inertial State Estimation

• Mueggler, E., Gallego, G., Rebecq, H., Scaramuzza, D.,
  Continuous-Time Visual-Inertial Trajectory Estimation with Event Cameras,
  (Under review), 2017.
• Mueggler et. al. IJRR 2017.
  The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM.
• Zhu, A., Atanasov, N., Daniilidis, K.,
  Event-based Visual Inertial Odometry,
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR) 2017. PDF, Supplementary material
• Rebecq, H., Horstschaefer, T., Scaramuzza, D.,
  Real-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization,
  British Machine Vision Conf. (BMVC), London, 2017. PDF, Appendix, YouTube
• Rosinol Vidal, A., Rebecq, H., Horstschaefer, T., Scaramuzza, D.,
  Hybrid, Frame and Event based Visual Inertial Odometry for Robust, Autonomous Navigation of Quadrotors,
  Under Review, 2017. PDF, YouTube

Visual Stabilization

• Delbruck, T., Villanueva, V., Longinotti, L.,
  Integration of dynamic vision sensor with inertial measurement unit for electronically stabilized event-based vision,
  IEEE Int. Symp. Circuits and Systems (ISCAS) 2014, 2636-2639. YouTube

Video Processing

• Brandli, C., Muller, L., Delbruck, T.,
  Real-time, high-speed video decompression using a frame- and event-based DAVIS sensor,
  IEEE Int. Symp. on Circuits and Systems (ISCAS), Melbourne VIC, 2014, pp. 686-689.

Pattern Recognition

• Lee, J., Delbruck, T., Park, P. K. J., Pfeiffer, M., Shin, C. W., Ryu, H., Kang, B. C.,
  Live demonstration: Gesture-Based remote control using stereo pair of dynamic vision sensors,
  IEEE Int. Symp. Circuits and Systems (ISCAS) 2012, Seoul, South Korea, pp. 736-740. PDF, YouTube
• Barua et. al. WACV 2016. Face recognition.
• Orchard, G., Meyer, C., Etienne-Cummings, R., Posch, C., Thakor, N., Benosman, R.,
  HFIRST: A Temporal Approach to Object Recognition,
  IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2015, 37(10):2028-2040. PDF
  • Code: HFIRST: A simple spiking neural network for recognition based on the canonical frame-based HMAX model.
• Moeys, D., Corradi F., Kerr, E., Vance, P., Das, G., Neil, D., Kerr, D., Delbruck, T.,
  Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network,
  IEEE Int. Conf. Event-Based Control Comm. and Signal Proc. (EBCCSP), Krakow, Poland, 2016. PDF, YouTube 1, YouTube 2
• Lagorce, X., Orchard, G., Gallupi, F., Shi, B., Benosman, R.,
  HOTS: A Hierarchy Of event-based Time-Surfaces for pattern recognition,
  IEEE Trans. Pattern Anal. Machine Intell. (TPAMI), 2017, 39(7):1346-1359.
• Clady et. al. FNINS,
  A Motion-Based Feature for Event-Based Pattern Recognition.
• Lungu, I.-A., Corradi, F., Delbruck, T.,
  Live Demonstration: Convolutional Neural Network Driven by Dynamic Vision Sensor Playing RoShamBo,
  IEEE Int. Symp. Circuits and Systems (ISCAS), Baltimore, MD, 2017. YouTube, Slides 36-39
• Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., Kusnitz, J., Debole, M., Esser, S., Delbruck, T., Flickner, M., Modha, D.,
  A Low Power, Fully Event-Based Gesture Recognition System,
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR) 2017. PDF.
  • YouTube: IBM Research demonstrates event-based gesture recognition using a brain-inspired chip

Control

• Delbruck, T. and Lichtsteiner, P.,
  Fast sensory motor control based on event-based hybrid neuromorphic-procedural system,
  IEEE Int. Symp. Circuits and Systems, New Orleans, LA, 2007, pp. 845-848.
• Conradt, J., Cook, M., Berner, R., Lichtsteiner, P., Douglas, R. J., Delbruck, T.,
  A Pencil Balancing Robot Using a Pair of AER Dynamic Vision Sensors,
  IEEE Int. Symp. Circuits and Systems (ISCAS) 2009, pp. 781-784, 2009. PDF, Poster, Project page, YouTube 1, YouTube 2, YouTube 3
• Conradt, J., Berner, R., Cook, M., Delbruck, T.,
  An embedded AER dynamic vision sensor for low-latency pole balancing,
  IEEE Int. Conf. Computer Vision Workshops (ICCVW), Kyoto, Japan, 2009. PDF
• Delbruck, T. and Lang, M.,
  Robotic Goalie with 3ms Reaction Time at 4% CPU Load Using Event-Based Dynamic Vision Sensor,
  Front. Neurosci. (2013) 7:223. PDF, YouTube
• Censi, A.,
  Efficient Neuromorphic Optomotor Heading Regulation,
  American Control Conference (ACC), Chicago, IL, 2015, pp. 3854-3861.
• Mueggler, E., Baumli, N., Fontana, F., Scaramuzza, D.,
  Towards Evasive Maneuvers with Quadrotors using Dynamic Vision Sensors,
  Eur. Conf. Mobile Robots (ECMR), Lincoln, 2015. PDF
• Delbruck, T., Pfeiffer, M., Juston, R., Orchard, G., Mueggler, E., Linares-Barranco, A., Tilden, M. W.,
  Human vs. computer slot car racing using an event and frame-based DAVIS vision sensor,
  IEEE Int. Symp. Circuits and Systems (ISCAS), Lisbon, 2015, pp. 2409-2412. YouTube 1, YouTube 2
• Moeys et. al. EBCCSP 2016. VISUALISE Predator/Prey Dataset.
• Vasco, V., Glover, A., Tirupachuri, Y., Solari, F., Chessa M., Bartolozzi C.,
  Vergence control with a neuromorphic iCub,
  IEEE Int. Conf. Humanoid Robotics (Humanoids), Cancun, Mexico, 2016, pp. 732-738.

Datasets and Simulators (sorted by topic)

• Several datasets from the Sensors group at INI (Institute of Neuroinformatics), Zurich:
  • DVS128 Dynamic Vision Sensor Silicon Retina data

Optical Flow

• DVS/DAVIS Optical Flow Dataset associated to the paper Rueckauer and Delbruck, FNINS 2016.
• Binas et. al. ICML 2017. DDD17: End-To-End DAVIS Driving Dataset.

Visual Odometry and SLAM

• Combined Dynamic Vision / RGB-D Dataset associated to the paper Weikersdorfer et. al. ICRA 2014.
• Barranco, F., Fermuller, C., Aloimonos, Y.,
  A Dataset for Visual Navigation with Neuromorphic Methods,
  Front. Neurosci. (2016), 10:49.
• E. Mueggler, H. Rebecq, G. Gallego, T. Delbruck, D. Scaramuzza,
  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, Dataset.
• Binas, J., Neil, D., Liu, S.-C., Delbruck, T.,
  DDD17: End-To-End DAVIS Driving Dataset,
  Int. Conf. Machine Learning, Sydney, Australia, PMLR 70, 2017. Dataset

Recognition

• 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
• 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

Software

Drivers

• 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)
• ROS (Robotic Operating System)
• YARP (Yet Another Robot Platform)

Calibration

• 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.
• 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.

Algorithms

• Several event-processing filters in the jAER (java Address-Event Representation) project

• A collection of tracking and detection algorithms using the YARP framework

• 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.

• Intensity-Image reconstruction

  • 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.

• 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.

Utilities

• 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.
• 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.

Neuromorphic Processors and Platforms

• Dynamic Neuromorphic Asynchronous Processor (DYNAP) by iniLabs
  • 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), Washington, DC, 2015, pp. 4.2.1-4.2.4. 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), Providence, RI, 2012, pp. 76-82.
• 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), Hong Kong, 2014, pp. 2862-2867. PDF

Workshops and Tutorials

• ICRA 2015 Workshop on Innovative Sensing for Robotics, with a focus on Neuromorphic Sensors.
• Event-Based Vision for High-Speed Robotics (slides) IROS 2015, Workshop on Alternative Sensing for Robot Perception.
• ICRA 2017 First International Workshop on Event-based Vision.
• The Telluride Neuromorphic Cognition Engineering Workshops.
• Capo Caccia Workshops toward Cognitive Neuromorphic Engineering.

Theses and Dissertations

• 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.
• 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
• 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
• 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.
• See also Theses from Delbruck's group at INI

People / Organizations

• Institute of NeuroInformatics (INI) of the University of Zurich (UZH) and ETH Zurich.
• iniLabs (Comerzialization of neuromorphic technology from INI).
• Dynamic Vision Sensor (DVS) - asynchronous temporal contrast silicon retina
• Robotics and Perception Group (RPG-UZH).

Contributing

Please see CONTRIBUTING for details.

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