The DOI matches its Crossref record, with a small difference in title or year; 230 of the 232 references it cites were found in a registry or an index.
Event-Based Vision: A Survey
Open accessAbstract
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of μs), very high dynamic range (140 dB versus 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“new computer vision algorithms are needed to process the unconventional output of neuromorphic sensors to unlock their potential (Gallego et al., 2022; Sanchez-Garcia et al., 2022).”
From Towards a Smart Bionic Eye : AI-powered artificial vision for the treatment of incurable blindness (2022) · cites this paper as Gallego et al., 2022
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
(1981) · Cited by 11,963
Thrun, S. (2002) · CACM · doi:10.1145/504729.504754 · Cited by 7,941
Computational modelling of visual attention
Itti, L., & Koch, C. (2001) · Nature Reviews Neuroscience · doi:10.1038/35058500 · Cited by 4,733
Machine Learning for High-Speed Corner Detection
Rosten, E., & Drummond, T. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_34 · Cited by 4,555
A million spiking-neuron integrated circuit with a scalable communication network and interface
Merolla, P. A., Arthur, J. V., Alvarez-Icaza, R., Cassidy, A. S., Sawada, J., Akopyan, F., Jackson, B. L., Imam, N., Chen, G., Nakamura, Y., et al. (2014) · Science · doi:10.1126/science.1254642 · Cited by 4,270
Self-citationStereo Processing by Semiglobal Matching and Mutual Information
Hirschmüller, H. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,199
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
Davies, M., Srinivasa, N., Lin, T.-H., Chinya, G. N., Cao, Y., Choday, S. H., Dimou, G. D., Joshi, P., Imam, N., Jain, S., et al. (2018) · IEEE Micro · doi:10.1109/mm.2018.112130359 · Cited by 4,186
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2624754 · Cited by 3,596
Self-citationImplications of rational inattention
Sims, C. (2003) · Journal of Monetary Economics · doi:10.1016/s0304-3932(03)00029-1 · Cited by 3,582
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.914337 · Cited by 2,485
An introduction to event-triggered and self-triggered control
Heemels, W. P. M. H., Johansson, K. H., & Paulo Tabuada, B. (2012) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2012.6425820 · Cited by 2,039
TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip
Akopyan, F., Sawada, J., Cassidy, A. S., Alvarez-Icaza, R., Arthur, J. V., Merolla, P. A., Imam, N., Nakamura, Y., Datta, P., Nam, G.-J., et al. (2015) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2015.2474396 · Cited by 1,691
Self-citationCooperative Computation of Stereo Disparity
Marr, D., & Poggio, T. (1976) · Science · doi:10.1126/science.968482 · Cited by 1,407
Furber, S. B., Galluppi, F., Temple, S., & Plana, L. A. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2304638 · Cited by 1,373
The Representation and Matching of Pictorial Structures
Fischler, M. A., & Elschlager, R. A. (1973) · IEEE Transactions on Computers · doi:10.1109/t-c.1973.223602 · Cited by 1,322
Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks
Wu, Y., Deng, L., Li, G., Zhu, J., & Shi, L. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00331 · Cited by 1,266
CMOS image sensors: electronic camera-on-a-chip
Fossum, E. R. (1997) · IEEE Transactions on Electron Devices · doi:10.1109/16.628824 · Cited by 1,207
A 240 × 180 130 dB 3 µs Latency Global Shutter Spatiotemporal Vision Sensor
Brändli, C., Berner, R., Yang, M., Liu, S., & Delbrück, T. (2014) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2014.2342715 · Cited by 1,149
Rueckauer, B., Lungu, I.-A., Hu, Y., Pfeiffer, M., & Liu, S. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00682 · Cited by 1,144
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S., & Pfeiffer, M. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280696 · Cited by 1,090
Computation of component image velocity from local phase information
Fleet, D. J., & Jepson, A. D. (1990) · International Journal of Computer Vision · doi:10.1007/bf00056772 · Cited by 1,085
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
Self-citationTraining Deep Spiking Neural Networks Using Backpropagation
Lee, J. H., Delbrück, T., & Pfeiffer, M. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00508 · Cited by 1,010
A Large-Scale Model of the Functioning Brain
Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., & Rasmussen, D. (2012) · Science · doi:10.1126/science.1225266 · Cited by 973
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Orchard, G., Jayawant, A., Cohen, G., & Thakor, N. V. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00437 · Cited by 935
Self-citationEnd-to-End Learning of Driving Models from Large-Scale Video Datasets
Xu, H., Gao, Y., Yu, F., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.376 · Cited by 884
Convolutional networks for fast, energy-efficient neuromorphic computing
Esser, S. K., Merolla, P. A., Arthur, J. V., Cassidy, A. S., Appuswamy, R., Andreopoulos, A., Berg, D. J., McKinstry, J. L., Melano, T., Barch, D. R., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604850113 · Cited by 814
Self-citationPosch, C., Matolin, D., & Wohlgenannt, R. (2010) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2010.2085952 · Cited by 797
Moradi, S., Qiao, N., Stefanini, F., & Indiveri, G. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2759700 · Cited by 662
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars
Maqueda, A. I., Loquercio, A., Gallego, G., Garcia, N., & Scaramuzza, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00568 · Cited by 651
Self-citationOverview of the SpiNNaker System Architecture
Furber, S. B., Lester, D., Plana, L. A., Garside, J. D., Painkras, E., Temple, S., & Brown, A. D. (2012) · IEEE Transactions on Computers · doi:10.1109/tc.2012.142 · Cited by 648
HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition
Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E., & Benosman, R. B. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2574707 · Cited by 648
Self-citationA space-sweep approach to true multi-image matching
Collins, R. T. (1996) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1996.517097 · Cited by 642
SLAYER: Spike Layer Error Reassignment in TimePreprint
Shrestha, S. B., & Orchard, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.08646 · Cited by 627
Self-citationMueggler, E., Rebecq, H., Gallego, G., Delbrück, T., & Scaramuzza, D. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917691115 · Cited by 612
Self-citationUnsupervised Event-Based Learning of Optical Flow, Depth, and Egomotion
Zhu, A. Z., Yuan, L., Chaney, K. P., & Daniilidis, K. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00108 · Cited by 594
Self-citationThe Multivehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
Zhu, A. Z., Thakur, D., Özaslan, T., Pfrommer, B., Kumar, V. R., & Daniilidis, K. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2800793 · Cited by 574
Self-citationRosinol, A., Rebecq, H., Horstschaefer, T., & Scaramuzza, D. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2793357 · Cited by 533
Self-citationHATS: Histograms of Averaged Time Surfaces for Robust Event-Based Object Classification
Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., & Benosman, R. B. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00186 · Cited by 526
Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output
Posch, C., Serrano‐Gotarredona, T., Linares-Barranco, B., & Delbrück, T. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2346153 · Cited by 502
Nengo: a Python tool for building large-scale functional brain models
Bekolay, T., Bergstra, J., Hunsberger, E., DeWolf, T., Stewart, T. C., Rasmussen, D., Choo, X., Voelker, A. R., & Eliasmith, C. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00048 · Cited by 501
EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Zhu, A. Z., Yuan, L., Chaney, K. P., & Daniilidis, K. (2018) · Robotics: Science and Systems · doi:10.15607/rss.2018.xiv.062 · Cited by 478
Self-citationEvents-To-Video: Bringing Modern Computer Vision to Event Cameras
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00398 · Cited by 462
Self-citationReal-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera
Kim, H., Leutenegger, S., & Davison, A. J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_21 · Cited by 438
Self-citationBenosman, R., Clercq, C., Lagorce, X., Ieng, S.-H., & Bartolozzi, C. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2273537 · Cited by 424
Self-citationEnd-to-End Learning of Representations for Asynchronous Event-Based Data
Gehrig, D., Loquercio, A., Derpanis, K. G., & Scaramuzza, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00573 · Cited by 421
Self-citationReal-time classification and sensor fusion with a spiking deep belief network
O'Connor, P., Neil, D., Liu, S., Delbrück, T., & Pfeiffer, M. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00178 · Cited by 416
EVO: A Geometric Approach to Event-Based 6-DOF Parallel Tracking and Mapping in Real Time
Rebecq, H., Horstschaefer, T., Gallego, G., & Scaramuzza, D. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2645143 · Cited by 413
Self-citationÅström, K. J. (2007) · Springer eBooks · doi:10.1007/978-3-540-74358-3_9 · Cited by 395
Event-Based Moving Object Detection and Tracking
Mitrokhin, A., Fermuller, C. M., Parameshwara, C. M., & Aloimonos, Y. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593805 · Cited by 381
Liu, S., & Delbrück, T. (2010) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2010.03.007 · Cited by 375
A biomorphic digital image sensor
Culurciello, E., Etienne‐Cummings, R., & Boahen, K. A. (2003) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2002.807412 · Cited by 374
Gallego, G., Rebecq, H., & Scaramuzza, D. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00407 · Cited by 355
Self-citationESIM: an Open Event Camera Simulator
Rebecq, H., Gehrig, D., & Scaramuzza, D. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-175993 · Cited by 339
Self-citationSerrano‐Gotarredona, T., & Linares-Barranco, B. (2013) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2012.2230553 · Cited by 335
Pérez‐Carrasco, J. A., Zhao, B., Serrano, C., Acha, B., Serrano‐Gotarredona, T., Chen, S., & Linares-Barranco, B. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.71 · Cited by 326
Serrano-Gotarredona, R., Oster, M., Lichtsteiner, P., Linares-Barranco, A., Paz-Vicente, R., Gómez-Rodríguez, F., Camuñas-Mesa, L. A., Berner, R., Rivas-Perez, M., Delbrück, T., et al. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2009.2023653 · Cited by 326
A 128 X 128 120db 30mw asynchronous vision sensor that responds to relative intensity change
Lichtsteiner, P., Posch, C., & Delbrück, T. (2006) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2006.1696265 · Cited by 323
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera
Pan, L., Scheerlinck, C., Yu, X., Hartley, R. I., Liu, M., & Dai, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00698 · Cited by 318
HFirst: A Temporal Approach to Object Recognition
Orchard, G., Meyer, C., Etienne‐Cummings, R., Posch, C., Thakor, N. V., & Benosman, R. B. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2392947 · Cited by 307
Self-citationSimultaneous Optical Flow and Intensity Estimation from an Event Camera
Bardow, P. A., Davison, A. J., & Leutenegger, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.102 · Cited by 306
Self-citationVLSI analogs of neuronal visual processing: a synthesis of form and function
Mahowald, M. A. (1992) · CaltechTHESIS (California Institute of Technology) · doi:10.7907/4bdw-fg34 · Cited by 304
Aimar, A., Mostafa, H., Calabrese, E., Ríos-Navarro, A., Tapiador-Morales, R., Lungu, I.-A., Milde, M. B., Corradi, F., Linares-Barranco, A., Liu, S., et al. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2852335 · Cited by 289
Motion history image: its variants and applications
Ahad, M. A. R., Tan, J. K., Kim, H., & Ishikawa, S. (2010) · Machine Vision and Applications · doi:10.1007/s00138-010-0298-4 · Cited by 283
EMVS: Event-Based Multi-View Stereo—3D Reconstruction with an Event Camera in Real-Time
Rebecq, H., Gallego, G., Mueggler, E., & Scaramuzza, D. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-017-1050-6 · Cited by 279
Self-citationRose, A. (1970) · Springer eBooks · doi:10.1007/978-1-4684-1854-5_4 · Cited by 278
Finateu, T., Niwa, A., Matolin, D., 土本, 航., Mascheroni, A., Reynaud, E., Mostafalu, P., Frederick, B., Chotard, L., LeGoff, F., et al. (2020) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc19947.2020.9063149 · Cited by 277
Mahowald, M. A., & Mead, C. (1991) · Scientific American · doi:10.1038/scientificamerican0591-76 · Cited by 276
Video to Events: Recycling Video Datasets for Event Cameras
Gehrig, D., Gehrig, M., Hidalgo‐Carrió, J., & Scaramuzza, D. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00364 · Cited by 275
Self-citationReal-time Visual-Inertial Odometry for Event Cameras using Keyframe-based Nonlinear Optimization
Rebecq, H., Horstschaefer, T., & Scaramuzza, D. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.16 · Cited by 274
Self-citationEvent-Based Visual Inertial Odometry
Zhu, A., Atanasov, N. A., & Daniilidis, K. (2017) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.616 · Cited by 263
Self-citationEvent-based, 6-DOF pose tracking for high-speed maneuvers
Mueggler, E., Huber, B., & Scaramuzza, D. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942940 · Cited by 260
Self-citationActivity-driven, event-based vision sensors
Delbrück, T., Linares-Barranco, B., Culurciello, E., & Posch, C. (2010) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2010.5537149 · Cited by 252
Fast Image Reconstruction with an Event Camera
Scheerlinck, C., Rebecq, H., Gehrig, D., Barnes, N. M., Mahony, R. E., & Scaramuzza, D. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093366 · Cited by 252
Self-citationSimultaneous Mosaicing and Tracking with an Event Camera
Kim, H., Handa, A., Benosman, R. B., Ieng, S.-H., & Davison, A. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.26 · Cited by 248
Self-citationEvent-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines
Neftci, E., Augustine, C., Paul, S., & Detorakis, G. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00324 · Cited by 244
EKLT: Asynchronous Photometric Feature Tracking Using Events and Frames
Gehrig, D., Rebecq, H., Gallego, G., & Scaramuzza, D. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01209-w · Cited by 244
Self-citationWang, L., Mostafavi, M., Ho, Y.-S., & Yoon, K.-J. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01032 · Cited by 240
Continuous-Time Intensity Estimation Using Event Cameras
Scheerlinck, C., Barnes, N. M., & Mahony, R. E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20873-8_20 · Cited by 236
Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model
Neckar, A., Fok, S., Benjamin, B. V., Stewart, T. C., Oza, N. N., Voelker, A. R., Eliasmith, C., Manohar, R., & Boahen, K. A. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2881432 · Cited by 231
Asynchronous frameless event-based optical flow
Benosman, R. B., Ieng, S.-H., Clercq, C., Bartolozzi, C., & Srinivasan, M. V. (2011) · Neural Networks · doi:10.1016/j.neunet.2011.11.001 · Cited by 229
Self-citationFrame-free dynamic digital vision
Delbrück, T. (2008) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-17620 · Cited by 229
Event-Based Control and Signal Processing
Heemels, W. P. M. H., Postoyan, R., Donkers, M. C. F., Teel, A. R., Anta, A., Tabuada, P., Nešić, D., & Miskowicz, M. (2015) · CRC Press eBooks · doi:10.1201/b19013 · Cited by 221
Robotic goalie with 3 ms reaction time at 4% CPU load using event-based dynamic vision sensor
Delbrück, T., & Lang, M. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00223 · Cited by 215
Continuous-Time Visual-Inertial Odometry for Event Cameras
Mueggler, E., Gallego, G., Rebecq, H., & Scaramuzza, D. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2858287 · Cited by 211
Self-citationEvent-Based, 6-DOF Camera Tracking from Photometric Depth Maps
Gallego, G., Lund, J. E. A., Mueggler, E., Rebecq, H., Delbrück, T., & Scaramuzza, D. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2769655 · Cited by 205
Self-citationEvent‐Based Neuromorphic Systems
Liu, S., Delbrück, T., Indiveri, G., Whatley, A., & Douglas, R. J. (2014) · Wiley eBooks · doi:10.1002/9781118927601 · Cited by 204
Paredes-Vallés, F., Scheper, K. Y. W., & de Croon, G. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2903179 · Cited by 203
Low-latency visual odometry using event-based feature tracks
Kueng, B., Mueggler, E., Gallego, G., & Scaramuzza, D. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7758089 · Cited by 202
Self-citation4.1 A 640×480 dynamic vision sensor with a 9µm pixel and 300Meps address-event representation
Bongki, S., Suh, Y., Kim, S., Jung, H., Kim, J., Shin, C.-W., Park, K., Lee, K., Park, J., Woo, J., et al. (2017) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2017.7870263 · Cited by 199
Asynchronous Corner Detection and Tracking for Event Cameras in Real Time
Alzugaray, I., & Chli, M. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2849882 · Cited by 197
Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
Blouw, P., Choo, X., Hunsberger, E., & Eliasmith, C. (2019) · Neuro Inspired Computational Elements Conference (NICE) · doi:10.1145/3320288.3320304 · Cited by 197
Fast Event-based Corner Detection
Mueggler, E., Bartolozzi, C., & Scaramuzza, D. (2017) · British Machine Vision Conference (BMVC) · doi:10.5244/c.31.33 · Cited by 195
Self-citationEvent-based feature tracking with probabilistic data association
Zhu, A., Atanasov, N. A., & Daniilidis, K. (2017) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2017.7989517 · Cited by 191
Self-citationAccurate Angular Velocity Estimation With an Event Camera
Gallego, G., & Scaramuzza, D. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2647639 · Cited by 185
Self-citationRajendran, B., Sebastian, A., Schmuker, M., Srinivasa, N., & Eleftheriou, E. S. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2933719 · Cited by 184
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
Munda, G., Reinbacher, C., & Pock, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1106-2 · Cited by 181
Spatial and temporal receptive fields of geniculate and cortical cells and directional selectivity
Valois, R. L. D., Cottaris, N. P., Mahon, L. E., Elfar, S. D., & Wilson, J. A. (2000) · Vision Research · doi:10.1016/s0042-6989(00)00210-8 · Cited by 179
A Burst-Mode Word-Serial Address-Event Link—I: Transmitter Design
Boahen, K. A. (2004) · IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications · doi:10.1109/tcsi.2004.830703 · Cited by 176
Fast event-based Harris corner detection exploiting the advantages of event-driven cameras
Vasco, V., Glover, A., & Bartolozzi, C. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759610 · Cited by 168
Self-citationAsynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking
Lagorce, X., Meyer, C., Ieng, S.-H., Filliat, D., & Benosman, R. B. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2352401 · Cited by 166
Asynchronous Event-Based Binocular Stereo Matching
Rogister, P., Benosman, R., Ieng, S.-H., Lichtsteiner, P., & Delbruck, T. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2011.2180025 · Cited by 166
Self-citationEmbedded Vision System for Real-Time Object Tracking using an Asynchronous Transient Vision Sensor
Litzenberger, M., Posch, C., Bauer, D., Belbachir, A. N., Schon, P., Kohn, B., & Garn, H. (2006) · IEEE Digital Signal Processing Workshop and IEEE Signal Processing Education Workshop (DSP/SPE) · doi:10.1109/dspws.2006.265448 · Cited by 166
Event-Based Motion Segmentation by Motion Compensation
Stoffregen, T., Gallego, G., Drummond, T. W., Kleeman, L., & Scaramuzza, D. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00734 · Cited by 162
Self-citationA pencil balancing robot using a pair of AER dynamic vision sensors
Conradt, J., Cook, M., Berner, R., Lichtsteiner, P., Douglas, R. J., & Delbrück, T. (2009) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2009.5117867 · Cited by 155
Self-citationFront and Back Illuminated Dynamic and Active Pixel Vision Sensors Comparison
Taverni, G., Moeys, D. P., Li, C.-H., Cavaco, C., Motsnyi, V., Bello, D. S. S., & Delbrück, T. (2018) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2018.2824899 · Cited by 151
Focus Is All You Need: Loss Functions for Event-Based Vision
Gallego, G., Gehrig, M., & Scaramuzza, D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01256 · Cited by 150
Self-citationAsynchronous Event-Based Visual Shape Tracking for Stable Haptic Feedback in Microrobotics
Ni, Z., Bolopion, A., Agnus, J., Benosman, R. B., & Régnier, S. (2012) · IEEE Transactions on Robotics · doi:10.1109/tro.2012.2198930 · Cited by 147
Semi-dense 3D Reconstruction with a Stereo Event Camera
Zhou, Y., Gallego, G., Rebecq, H., Kneip, L., Li, H., & Scaramuzza, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_15 · Cited by 145
Self-citationInteracting maps for fast visual interpretation
Cook, M., Gugelmann, L., Jug, F., Krautz, C., & Steger, A. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033299 · Cited by 142
Event-based 3D SLAM with a depth-augmented dynamic vision sensor
Weikersdorfer, D., Adrian, D. B., Cremers, D., & Conradt, J. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6906882 · Cited by 138
Self-citationFast sensory motor control based on event-based hybrid neuromorphic-procedural system
Delbrück, T., & Lichtsteiner, P. (2007) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2007.378038 · Cited by 138
Neuromorphic Spiking Neural Networks and Their Memristor-CMOS Hardware Implementations
Camuñas-Mesa, L. A., Linares-Barranco, B., & Serrano‐Gotarredona, T. (2019) · Materials · doi:10.3390/ma12172745 · Cited by 136
Toward real-time particle tracking using an event-based dynamic vision sensor
Drazen, D., Lichtsteiner, P., Hafliger, P. D., Delbrück, T., & Jensen, A. (2011) · Experiments in Fluids · doi:10.1007/s00348-011-1207-y · Cited by 135
Direct face detection and video reconstruction from event cameras
Miyatani, Y., Barua, S., & Veeraraghavan, A. (2016) · 2016 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2016.7477561 · Cited by 133
DVS Benchmark Datasets for Object Tracking, Action Recognition, and Object Recognition
Hu, Y., Liu, H., Pfeiffer, M., & Delbrück, T. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00405 · Cited by 133
Yang, M., Liu, S., & Delbrück, T. (2015) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2015.2425886 · Cited by 132
Lifetime estimation of events from Dynamic Vision Sensors
Mueggler, E., Förster, C., Baumli, N., Gallego, G., & Scaramuzza, D. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139876 · Cited by 130
Self-citationLow-latency event-based visual odometry
Censi, A., & Scaramuzza, D. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6906931 · Cited by 130
Self-citationA Sensitive Dynamic and Active Pixel Vision Sensor for Color or Neural Imaging Applications
Moeys, D. P., Corradi, F., Li, C.-H., Bamford, S. A., Longinotti, L., Voigt, F. F., Berry, S., Taverni, G., Helmchen, F., & Delbrück, T. (2017) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2759783 · Cited by 130
Steering a predator robot using a mixed frame/event-driven convolutional neural network
Moeys, D. P., Corradi, F., Kerr, E., Vance, P. J., Das, G. P., Neil, D., Kerr, D., & Delbrück, T. (2016) · International Conference on Event-based Control, Communication, and Signal Processing · doi:10.1109/ebccsp.2016.7605233 · Cited by 128
Stromatias, E., Soto, M., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00350 · Cited by 124
Real-time, high-speed video decompression using a frame- and event-based DAVIS sensor
Brändli, C., Müller, L. K., & Delbrück, T. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865228 · Cited by 121
Asynchronous event‐based high speed vision for microparticle tracking
Ni, Z., Pacoret, C., Benosman, R. B., Ieng, S.-H., & Régnier, S. (2011) · Journal of Microscopy · doi:10.1111/j.1365-2818.2011.03565.x · Cited by 120
A 1280×960 Dynamic Vision Sensor with a 4.95-μm Pixel Pitch and Motion Artifact Minimization
Suh, Y., Choi, S., Ito, M., Kim, J., Lee, Y., Seo, J., Jung, H., Yeo, D., Namgung, S., Bong, J., et al. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9180436 · Cited by 120
Asynchronous event-based corner detection and matching
Clady, X., Ieng, S.-H., & Benosman, R. B. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.02.013 · Cited by 119
Posch, C., Matolin, D., & Wohlgenannt, R. (2010) · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · doi:10.1109/isscc.2010.5433973 · Cited by 119
Simultaneous Localization and Mapping for Event-Based Vision Systems
Weikersdorfer, D., Hoffmann, R., & Conradt, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39402-7_14 · Cited by 117
Self-citationA spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems
Osswald, M., Ieng, S.-H., Benosman, R. B., & Indiveri, G. (2017) · Scientific Reports · doi:10.1038/srep40703 · Cited by 113
Speed Invariant Time Surface for Learning to Detect Corner Points With Event-Based Cameras
Manderscheid, J., Sironi, A., Bourdis, N., Migliore, D., & Lepetit, V. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01049 · Cited by 112
Rueckauer, B., & Delbrück, T. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00176 · Cited by 111
Live Demonstration: CeleX-V: A 1M Pixel Multi-Mode Event-Based Sensor
Chen, S., & Guo, M. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00214 · Cited by 111
EventNet: Asynchronous Recursive Event Processing
Sekikawa, Y., Hara, K., & Saitô, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00401 · Cited by 110
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
Mitrokhin, A., Ye, C., Fermuller, C. M., Aloimonos, Y., & Delbrück, T. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968520 · Cited by 108
Estimation of Vehicle Speed Based on Asynchronous Data from a Silicon Retina Optical Sensor
Litzenberger, M., Kohn, B., Belbachir, A. N., Donath, N., Gritsch, G., Garn, H., Posch, C., & Schraml, S. (2006) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2006.1706816 · Cited by 107
Feature detection and tracking with the dynamic and active-pixel vision sensor (DAVIS)
Tedaldi, D., Gallego, G., Mueggler, E., & Scaramuzza, D. (2016) · International Conference on Event-Based Control, Communication, and Signal Processing · doi:10.1109/ebccsp.2016.7605086 · Cited by 107
Self-citationTemperature and Parasitic Photocurrent Effects in Dynamic Vision Sensors
Nozaki, Y., & Delbrück, T. (2017) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2017.2717848 · Cited by 103
On event-based optical flow detection
Brosch, T., Tschechne, S., & Heiko Neumann, B. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00137 · Cited by 100
Real-Time 6DOF Pose Relocalization for Event Cameras With Stacked Spatial LSTM Networks
Nguyen, A., Do, T.-T., Caldwell, D. G., & Tsagarakis, N. G. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00207 · Cited by 100
Spiking Optical Flow for Event-Based Sensors Using IBM's TrueNorth Neurosynaptic System
Haessig, G., Cassidy, A. S., Alvarez-Icaza, R., Benosman, R. B., & Orchard, G. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2834558 · Cited by 95
Self-citationA 64x64 aer logarithmic temporal derivative silicon retina
Lichtsteiner, P., & Delbrück, T. (2005) · Research in Microelectronics and Electronics, 2005 PhD · doi:10.1109/rme.2005.1542972 · Cited by 93
Realtime Time Synchronized Event-Based Stereo
Zhu, A. Z., Chen, Y., & Daniilidis, K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_27 · Cited by 93
Self-citationEffective sensor fusion with event-based sensors and deep network architectures
Neil, D., & Liu, S. (2016) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2016.7539039 · Cited by 92
Event-based Sensing for Space Situational Awareness
Cohen, G., Afshar, S., Morreale, B., Bessell, T., Wabnitz, A., Rutten, M. G., & André, V. S. (2019) · The Journal of the Astronautical Sciences · doi:10.1007/s40295-018-00140-5 · Cited by 92
Event-driven ball detection and gaze fixation in clutter
Glover, A., & Bartolozzi, C. (2016) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2016.7759345 · Cited by 88
Self-citationEvent-based, Direct Camera Tracking from a Photometric 3D Map using Nonlinear Optimization
Bryner, S. F., Gallego, G., Rebecq, H., & Scaramuzza, D. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794255 · Cited by 88
Self-citationMC3D: Motion Contrast 3D Scanning
Matsuda, N., Cossairt, O. S., & Gupta, M. (2015) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccphot.2015.7168370 · Cited by 85
Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas
Lee, J. H., Delbrück, T., Pfeiffer, M., Park, P. K. J., Shin, C.-W., Ryu, H., & Kang, B. C. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2308551 · Cited by 84
Robust visual tracking with a freely-moving event camera
Glover, A., & Bartolozzi, C. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206226 · Cited by 84
Self-citationSpatiotemporal multiple persons tracking using Dynamic Vision Sensor
Piatkowska, E., Belbachir, A., Schraml, S., & Gelautz, M. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops · doi:10.1109/cvprw.2012.6238892 · Cited by 82
Adaptive Time-Slice Block-Matching Optical Flow Algorithm for Dynamic Vision Sensors
Liu, M., & Delbrück, T. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-168589 · Cited by 78
Event-Driven Sensing for Efficient Perception: Vision and Audition Algorithms
Liu, S., Rueckauer, B., Ceolini, E., Huber, A. E. G., & Delbrück, T. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2928127 · Cited by 77
Unsupervised Learning of Dense Optical Flow, Depth and Egomotion with Event-Based Sensors
Ye, C., Mitrokhin, A., Fermuller, C. M., Yorke, J. A., & Aloimonos, Y. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341224 · Cited by 77
Visual Tracking Using Neuromorphic Asynchronous Event-Based Cameras
Ni, Z., Ieng, S.-H., Posch, C., Régnier, S., & Benosman, R. B. (2015) · Neural Computation · doi:10.1162/neco_a_00720 · Cited by 73
Real-time panoramic tracking for event cameras
Reinbacher, C., Munda, G., & Pock, T. (2017) · IEEE International Conference on Computational Photography (ICCP) · doi:10.1109/iccphot.2017.7951488 · Cited by 73
Neuromorphic Stereo Vision: A Survey of Bio-Inspired Sensors and Algorithms
Steffen, L., Reichard, D., Weinland, J., Kaiser, J., Roennau, A., & Dillmann, R. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00028 · Cited by 73
Event-based particle filtering for robot self-localization
Weikersdorfer, D., & Conradt, J. (2012) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2012.6491077 · Cited by 72
Self-citationStar Tracking Using an Event Camera
Chin, T.-J., Bagchi, S., Eriksson, A. P., & André, V. S. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00208 · Cited by 72
A spiking neural network architecture for visual motion estimation
Orchard, G., Benosman, R. B., Etienne‐Cummings, R., & Thakor, N. V. (2013) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2013.6679698 · Cited by 71
Self-citationA Motion-Based Feature for Event-Based Pattern Recognition
Clady, X., Maro, J.-M., Barré, S., & Benosman, R. B. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00594 · Cited by 69
Live demonstration: Convolutional neural network driven by dynamic vision sensor playing RoShamBo
Lungu, I.-A., Corradi, F., & Delbrück, T. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050403 · Cited by 67
Dynamic stereo vision system for real-time tracking
Schraml, S., Belbachir, A. N., Milosevic, N., & Schon, P. (2010) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2010.5537289 · Cited by 66
Live demonstration: A 768 × 640 pixels 200Meps dynamic vision sensor
Guo, M., Huang, J., & Chen, S. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050397 · Cited by 66
Bio-inspired Stereo Vision System with Silicon Retina Imagers
Kogler, J., Sulzbachner, C., & Kubinger, W. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04667-4_18 · Cited by 65
A Low Power, High Throughput, Fully Event-Based Stereo System
Andreopoulos, A., Kashyap, H. J., Nayak, T. K., Amir, A., & Flickner, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00786 · Cited by 65
Asynchronous Spatial Image Convolutions for Event Cameras
Scheerlinck, C., Barnes, N. M., & Mahony, R. E. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2893427 · Cited by 65
Event-based 3D reconstruction from neuromorphic retinas
Carneiro, J., Ieng, S.-H., Posch, C., & Benosman, R. (2013) · Neural Networks · doi:10.1016/j.neunet.2013.03.006 · Cited by 64
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
Reinbacher, C., Graber, G., & Pock, T. (2016) · British Machine Vision Conference (BMVC) · doi:10.5244/c.30.9 · Cited by 63
On the use of orientation filters for 3D reconstruction in event-driven stereo vision
Camuñas-Mesa, L. A., Serrano‐Gotarredona, T., Ieng, S.-H., Benosman, R. B., & Linares-Barranco, B. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2014.00048 · Cited by 60
A 100,000 fps vision sensor with embedded 535GOPS/W 256×256 SIMD processor array
Carey, S. J., Lopich, A., Barr, D. R. W., Wang, B., & Dudek, P. (2013) · Symposium on VLSI Circuits · Cited by 60
Contour Motion Estimation for Asynchronous Event-Driven Cameras
Barranco, F., Fermuller, C. M., & Aloimonos, Y. (2014) · Proceedings of the IEEE · doi:10.1109/jproc.2014.2347207 · Cited by 59
Asynchronous Event-Based Hebbian Epipolar Geometry
Benosman, R. B., Ieng, S.-H., Rogister, P., & Posch, C. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2167239 · Cited by 58
Vertical landing for micro air vehicles using event‐based optical flow
Hordijk, B. J. P., Scheper, K. Y. W., & de Croon, G. (2017) · Journal of Field Robotics · doi:10.1002/rob.21764 · Cited by 58
An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking
Valeiras, D. R., Lagorce, X., Clady, X., Bartolozzi, C., Ieng, S.-H., & Benosman, R. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2401834 · Cited by 57
Self-citationEvent-Based Stereo Matching Approaches for Frameless Address Event Stereo Data
Kogler, J., Humenberger, M., & Sulzbachner, C. (2011) · International Symposium on Visual Computing · doi:10.1007/978-3-642-24028-7_62 · Cited by 53
Piatkowska, E., Belbachir, A. N., & Gelautz, M. (2013) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.13 · Cited by 52
Data and Power Efficient Intelligence with Neuromorphic Learning Machines
Neftci, E. (2018) · iScience · doi:10.1016/j.isci.2018.06.010 · Cited by 52
Event-Based Stereo Depth Estimation Using Belief Propagation
Xie, Z., Chen, S., & Orchard, G. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00535 · Cited by 51
Self-citationHigh Speed and High Dynamic Range Video with an Event Camera
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2963386 · Cited by 50
Self-citationWhat Can Neuromorphic Event-Driven Precise Timing Add to Spike-Based Pattern Recognition?
Akolkar, H., Meyer, C., Clady, X., Marre, O., Bartolozzi, C., Panzeri, S., & Benosman, R. B. (2015) · Neural Computation · doi:10.1162/neco_a_00703 · Cited by 49
Self-citationSimultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision SensorPreprint
Stoffregen, T., & Kleeman, L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12326 · Cited by 46
Mahowald, M. A. (1994) · Springer eBooks · doi:10.1007/978-1-4615-2724-4_2 · Cited by 45
Independent motion detection with event-driven cameras
Vasco, V., Glover, A., Mueggler, E., Scaramuzza, D., Natale, L., & Bartolozzi, C. (2017) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar.2017.8023661 · Cited by 44
Self-citationEvent-based Camera Pose Tracking using a Generative Event ModelPreprint
Gallego, G., Förster, C., Mueggler, E., & Scaramuzza, D. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1510.01972 · Cited by 44
Self-citationAsynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas
Firouzi, M., & Conradt, J. (2015) · Neural Processing Letters · doi:10.1007/s11063-015-9434-5 · Cited by 42
Self-citationAddress-Event Based Stereo Vision with Bio-Inspired Silicon Retina Imagers
Kogler, J., Sulzbachner, C., Humenberger, M., & Eibensteiner, F. (2011) · InTech eBooks · doi:10.5772/12941 · Cited by 40
Spiking Cooperative Stereo-Matching at 2 ms Latency with Neuromorphic Hardware
Dikov, G., Firouzi, M., Röhrbein, F., Conradt, J., & Richter, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-63537-8_11 · Cited by 39
Self-citationDelbrück, T., Villanueva, V., & Longinotti, L. (2014) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2014.6865714 · Cited by 38
ConvNets experiments on SpiNNaker
Serrano‐Gotarredona, T., Linares-Barranco, B., Galluppi, F., Plana, L. A., & Furber, S. B. (2015) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2015.7169169 · Cited by 38
Neuromorphic Event-Based Generalized Time-Based Stereovision
Ieng, S.-H., Carneiro, J., Osswald, M., & Benosman, R. B. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00442 · Cited by 38
Live demonstration: Gesture-based remote control using stereo pair of dynamic vision sensors
Lee, J., Delbrück, T., Park, P. K. J., Pfeiffer, M., Shin, C.-W., Ryu, H., & Kang, B. C. (2012) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2012.6272144 · Cited by 37
Event-driven visual attention for the humanoid robot iCub
Rea, F., Giorgio Metta, B., & Bartolozzi, C. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00234 · Cited by 36
Self-citationLive demonstration: Behavioural emulation of event-based vision sensors
Katz, M. L., Nikolić, K., & Delbrück, T. (2012) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2012.6272143 · Cited by 35
Event-driven sensing and processing for high-speed robotic vision
Camuñas-Mesa, L. A., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2014) · IEEE Biomedical Circuits and Systems Conference (BioCAS) · doi:10.1109/biocas.2014.6981776 · Cited by 34
Active Perception With Dynamic Vision Sensors. Minimum Saccades With Optimum Recognition
Yousefzadeh, A., Orchard, G., Serrano‐Gotarredona, T., & Linares-Barranco, B. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2018.2834428 · Cited by 33
Self-citationTime-derivative adaptive silicon photoreceptor array
Delbrück, T., & Mead, C. (1991) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.49323 · Cited by 27
Censi, A., Mueller, E., Frazzoli, E., & Soatto, S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139657 · Cited by 26
Self-citation
Flagged first, then by citations
2 of the 232 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- Delbruck, Neuromorphic vision sensing and processing, 7
- Lichtsteiner, 64 x 64 event-driven logarithmic temporal derivative silicon retina, 157
- Delbruck, V2E: From video frames to realistic DVS event camera streams, arXiv:2006.07722v1 · arXiv
- Berner, A 240x180 10mw 12$\mu$μs latency sparse-output vision sensor for mobile applications, C186
- Binas, DDD17: End-to-end DAVIS driving dataset, 1
- Szeliski, 2010, Computer Vision: Algorithms and Applications
- Kahneman, 2011, Thinking, Fast and Slow
Cited by
Sorted by citations
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
Training Spiking Neural Networks Using Lessons From Deep Learning
Eshraghian, J. K., Ward, M., Neftci, E., Wang, X., Lenz, G., Dwivedi, G., Bennamoun, M., Jeong, D. S., & Lu, W. D. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3308088 · Cited by 1,106
Advancing Neuromorphic Computing With Loihi: A Survey of Results and Outlook
Davies, M., Wild, A., Orchard, G., Sandamirskaya, Y., Guerra, G. A. F., Joshi, P., Plank, P., & Risbud, S. R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3067593 · Cited by 767
Self-citationPerception and sensing for autonomous vehicles under adverse weather conditions: A survey
Zhang, Y., Carballo, A., Yang, H., & Takeda, K. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.021 · Cited by 715
DSEC: A Stereo Event Camera Dataset for Driving Scenarios
Gehrig, M., Aarents, W., Gehrig, D., & Scaramuzza, D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3068942 · Cited by 493
Self-citation
Metadata
- Authors
- Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., & Scaramuzza, D.
- Affiliations
- Einstein Center Digital Future (DE)
- Intel (United States) (US)
- Italian Institute of Technology (IT)
- IBM (United States) (US)
- ETH Zurich (CH)
- Imperial College London (GB)
- University of Pennsylvania (US)
- University of Zurich (CH)
- ORCID
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2022-01-01
- Volume
- 44
- Issue
- 1
- Pages
- 154-180
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3040838455
- Cited by
- 2,566
- References
- 232
- Citations by year
- 2026: 553 · 2025: 648 · 2024: 510 · 2023: 363 · 2022: 258
- FWCI
- 17.31
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- SNSF-ERC
- Swiss National Science Foundation
- National Center of Competence in Research Robotics
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › CCD and CMOS Imaging Sensors
- Keywords
- event cameras, event-based vision, bio-inspired sensors, high dynamic range, high temporal resolution, event processing, optical flow, 3D reconstruction, image segmentation, object recognition, spiking neural networks, low-power vision, robot vision
- MeSH terms
- Algorithms, Robotics, Neural Networks, Computer
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
