High Speed and High Dynamic Range Video with an Event Camera
Open accessAbstract
Event cameras are novel sensors that report brightness changes in the form of a stream of asynchronous "events" instead of intensity frames. They offer significant advantages with respect to conventional cameras: high temporal resolution, high dynamic range, and no motion blur. While the stream of events encodes in principle the complete visual signal, the reconstruction of an intensity image from a stream of events is an ill-posed problem in practice. Existing reconstruction approaches are based on hand-crafted priors and strong assumptions about the imaging process as well as the statistics of natural images. In this work we propose to learn to reconstruct intensity images from event streams directly from data instead of relying on any hand-crafted priors. We propose a novel recurrent network to reconstruct videos from a stream of events, and train it on a large amount of simulated event data. During training we propose to use a perceptual loss to encourage reconstructions to follow natural image statistics. We further extend our approach to synthesize color images from color event streams. Our quantitative experiments show that our network surpasses state-of-the-art reconstruction methods by a large margin in terms of image quality ( ), while comfortably running in real-time. We show that the network is able to synthesize high framerate videos ( frames per second) of high-speed phenomena (e.g., a bullet hitting an object) and is able to provide high dynamic range reconstructions in challenging lighting conditions. As an additional contribution, we demonstrate the effectiveness of our reconstructions as an intermediate representation for event data. We show that off-the-shelf computer vision algorithms can be applied to our reconstructions for tasks such as object classification and visual-inertial odometry and that this strategy consistently outperforms algorithms that were specifically designed for event data. We release the reconstruction code, a pre-trained model and the datasets to enable further research.
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
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 event-to-image model [8] reconstructs a detailed image that reveals personal identity information.
“As illustrated in Figure 1(a), event-toimage [8] model reconstruct a detailed image that reveals personal identity information; consequently, this implies that event cameras can no longer be deemed inherently privacypreserving.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [8]
The Introduction (p. 2) says the model reconstructs high-quality images from event data, and §5.2 (p. 8) reports reconstructing an entire scene in a selfie sequence. Our copy does not establish that the reconstructed image reveals personal identity information; that is an additional inference, not a finding the paper reports. (LLM, conf=0.82)
Experimental — accuracy not yet measuredEvent cameras can no longer be deemed inherently privacy-preserving.
“As illustrated in Figure 1(a), event-toimage [8] model reconstruct a detailed image that reveals personal identity information; consequently, this implies that event cameras can no longer be deemed inherently privacypreserving.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [8]
The Evaluation (p. 6) reports detailed image reconstructions, and Section 5.2 (p. 8) describes reconstructing an entire scene from a selfie sequence. The paper does not establish that the images reveal personal identity information or assess whether event cameras are privacy-preserving; those are the citing authors’ inferences. (LLM, conf=0.85)
Experimental — accuracy not yet measuredE2VID translates a continuous stream of events into a sequence of images.
“E2VID translates a continuous stream of events into a sequence of images.”
From Event Anonymization: Privacy-Preserving Person Re-Identification and Pose Estimation in Event-Based Vision (2024) · cites this paper as [8]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623 · see citing works
(2017) · Cited by 11,625 · see citing works
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283 · see citing works
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
Aharon, M., Elad, M., & Bruckstein, A. M. (2006) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2006.881199 · Cited by 9,464 · see citing works
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709 · see citing works
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Qin, T., Li, P., & Shen, S. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853729 · Cited by 4,669 · see citing works
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017) · 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.2017.179 · Cited by 3,177 · see citing works
One-shot learning of object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.79 · Cited by 3,029 · see citing works
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566 · see citing works
Self-citationA 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 · see citing works
A Multi-State Constraint Kalman Filter for Vision-aided Inertial Navigation
Mourikis, A. I., & Roumeliotis, S. I. (2007) · Proceedings 2007 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2007.364024 · Cited by 1,951 · see citing works
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Graham, B., Engelcke, M., & Maaten, L. V. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00961 · Cited by 1,788 · see citing works
Keyframe-based visual–inertial odometry using nonlinear optimization
Leutenegger, S., Lynen, S., Bosse, M. C., Siegwart, R., & Furgale, P. (2014) · The International Journal of Robotics Research · doi:10.1177/0278364914554813 · Cited by 1,718 · see citing works
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry
Forster, C., Carlone, L., Dellaert, F., & Scaramuzza, D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2597321 · Cited by 1,502 · see citing works
Self-citationA 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 · see citing works
Robust visual inertial odometry using a direct EKF-based approach
Bloesch, M., Omari, S., Hutter, M., & Siegwart, R. (2015) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353389 · Cited by 934 · see citing works
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Orchard, G., eOrchard, G., eJayawant, A., Cohen, G., & eThakor, N. (2015) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.3389/fnins.2015.00437 · Cited by 893 · see citing works
Posch, C., Matolin, D., & Wohlgenannt, R. (2010) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2010.2085952 · Cited by 797 · see citing works
A Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry
Zhang, Z., & Scaramuzza, D. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593941 · Cited by 687 · see citing works
Self-citationHOTS: 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 617 · see citing works
Mueggler, E., Rebecq, H., Gallego, G., Delbrück, T., & Scaramuzza, D. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917691115 · Cited by 612 · see citing works
Self-citationGallego, G., Delbrück, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.3929/ethz-b-000521357 · Cited by 590 · see citing works
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 · see citing works
Rosinol, A., Rebecq, H., Horstschaefer, T., & Scaramuzza, D. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2793357 · Cited by 533 · see citing works
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 496 · see citing works
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 · see citing works
Real-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 · see citing works
Events-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 437 · see citing works
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 · see citing works
Robust Visual Inertial Odometry Using a Direct EKF-Based Approach
Bloesch, M., Omari, S., Hutter, M., et al. (2015) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-a-010566547 · Cited by 424 · see citing works
A Benchmark Comparison of Monocular Visual-Inertial Odometry Algorithms for Flying Robots
Delmerico, J., & Scaramuzza, D. (2018) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2018.8460664 · Cited by 421 · see citing works
Self-citationLearning Blind Video Temporal Consistency
Lai, W., Huang, J., Wang, O., Shechtman, E., Yumer, E., & Yang, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01267-0_11 · Cited by 414 · see citing works
ESIM: 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 351 · see citing works
Self-citationHFirst: 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 314 · see citing works
Simultaneous 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 · see citing works
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 284 · see citing works
Event-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 · see citing works
Event-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 · see citing works
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 253 · see citing works
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 · see citing works
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 · see citing works
Event-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 · see citing works
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 · see citing works
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 · see citing works
Vidal, A. R., Rebecq, H., Horstschaefer, T., et al. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-150437 · Cited by 176 · see citing works
Self-citationKeyframe-based Visual-Inertial SLAM using Nonlinear Optimization
Leutenegger, S., Furgale, P., Rabaud, V., Chli, M., Konolige, K., & Siegwart, R. (2013) · doi:10.3929/ethz-b-000236658 · Cited by 169 · see citing works
Asynchronous, Photometric Feature Tracking Using Events and Frames
Gehrig, D., Rebecq, H., Gallego, G., & Scaramuzza, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_46 · Cited by 157 · see citing works
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 · see citing works
Front 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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Unsupervised Event-Based Optical Flow Using Motion Compensation
Zhu, A. Z., Yuan, L., Chaney, K. P., & Daniilidis, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11024-6_54 · Cited by 107 · see citing works
Semi-dense 3D Reconstruction with a Stereo Event Camera
Zhou, Y. B., Gallego, G., Rebecq, H., Kneip, L., Li, H., & Scaramuzza, D. (2018) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-176054 · Cited by 58 · see citing works
Self-citationColor temporal contrast sensitivity in dynamic vision sensors
Moeys, D. P., Li, C.-H., Martel, J., Bamford, S. A., Longinotti, L., Motsnyi, V., Bello, D. S. S., & Delbrück, T. (2017) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2017.8050412 · Cited by 32 · see citing works
Comment on “Temperature and Parasitic Photocurrent Effects in Dynamic Vision Sensors”
Xu, J., Zou, J., & Gao, Z. (2018) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2018.2833106 · Cited by 7 · see citing works
CED: Color Event Camera Dataset
Scheerlinck, C., Rebecq, H., Stoffregen, T., Barnes, N. M., Mahony, R. E., & Scaramuzza, D. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00215
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- [22][22] A. Z. Zhu, L. Yuan, K. Chaney, and K. Daniilidis, 'Unsupervised eventbased optical flow using motion compensation,' in Eur. Conf. Comput. Vis. Workshops (ECCVW) , 2018. -
- [25][25] C. Brandli, R. Berner, M. Yang, S.-C. Liu, and T. Delbruck, 'A 240x180 130dB 3us latency global shutter spatiotemporal vision sensor,' IEEE J. Solid-State Circuits , vol. 49, no. 10, pp. 2333-2341, 2014. -
- [41][41] B. Son, Y. Suh, S. Kim, H. Jung, J.-S. Kim, C. Shin, K. Park, K. Lee, J. Park, J. Woo, Y. Roh, H. Lee, Y. Wang, I. Ovsiannikov, and H. Ryu, 'A 640x480 dynamic vision sensor with a 9um pixel and 300Meps addressevent representation,' in IEEE Intl. Solid-State Circuits Conf. (ISSCC) , 2017. -
Cited by
Sorted by citations
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566 · see citing works
Self-citationVideo to Events: Recycling Video Datasets for Event Cameras
Gehrig, D., Gehrig, M., Hidalgo‐Carrió, J., & Scaramuzza, D. (2020) · Zurich Open Repository and Archive (University of Zurich) · doi:10.1109/cvpr42600.2020.00364 · Cited by 246 · see citing works
Self-citationEventNeRF: Neural Radiance Fields from a Single Colour Event Camera
Rudnev, V., Elgharib, M. A., Theobalt, C., & Golyanik, V. (2023) · 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/cvpr52729.2023.00483 · Cited by 94 · see citing works
EventZoom: Learning to Denoise and Super Resolve Neuromorphic Events
Duan, P., Wang, Z. W., Zhou, X., Ma, Y., & Shi, B. (2021) · 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/cvpr46437.2021.01263 · Cited by 91 · see citing works
Learning to Reconstruct High Speed and High Dynamic Range Videos from Events
Zou, Y., Zheng, Y., Takatani, T., & Fu, Y. (2021) · 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/cvpr46437.2021.00206 · Cited by 76 · see citing works
A Survey on Deep Stereo Matching in the Twenties
Tosi, F., Bartolomei, L., & Poggi, M. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-024-02331-0 · Cited by 70 · see citing works
Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification
Ahmad, S., Scarpellini, G., Morerio, P., & Bue, A. D. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00052 · Cited by 54 · see citing works
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image Translation
Wang, L., Chae, Y., & Yoon, K. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00214 · Cited by 42 · see citing works
Person Re-Identification without Identification via Event Anonymization
Ahmad, S., Morerio, P., & Del Bue, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01022 · Cited by 39 · see citing works
Deep Event Stereo Leveraged by Event-to-Image Translation
Ahmed, S. H., Jang, H. W., Uddin, S. M. N., & Jung, Y. J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i2.16171 · Cited by 36 · see citing works
Lifting Monocular Events to 3D Human Poses
Scarpellini, G., Morerio, P., & Del Bue, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00150 · Cited by 31 · see citing works
Event-driven retinomorphic photodiode with bio-plausible temporal dynamics
Lin, Q., Li, C., Xiong, H., Zhang, M., Qiao, J., Wu, J., Yang, L., Wang, S., Chen, H., Wei, Y., et al. (2025) · Nature Nanotechnology · doi:10.1038/s41565-025-01973-6 · Cited by 29 · see citing works
Hybrid Deblur Net: Deep Non-Uniform Deblurring With Event Camera
Zhang, L., Zhang, H., Chen, J., & Wang, L. (2020) · IEEE Access · doi:10.1109/access.2020.3015759 · Cited by 28 · see citing works
On-board processing for autonomous drone racing: An overview
Rojas-Perez, L. O., & Martínez-Carranza, J. (2021) · Integration · doi:10.1016/j.vlsi.2021.04.007 · Cited by 28 · see citing works
Experimental Comparison between Event and Global Shutter Cameras
Holešovský, O., Škoviera, R., Hlaváč, V., & Vítek, R. (2021) · Sensors · doi:10.3390/s21041137 · Cited by 25 · see citing works
Learning to Deblur and Generate High Frame Rate Video with an Event CameraPreprint
Haoyu, C., Teng, M., Shi, B., YIzhou, W., & Tiejun, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.00847 · Cited by 23 · see citing works
Dual Memory Aggregation Network for Event-Based Object Detection with Learnable Representation
Wang, D., Jia, X., Zhang, Y., Zhang, X., Wang, Y., Zhang, Z., Wang, D., & Lu, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25346 · Cited by 19 · see citing works
Learning to Super-resolve Dynamic Scenes for Neuromorphic Spike Camera
Zhao, J. H., Xiong, R., Zhang, J., Zhao, R. Q., Liu, H., & Huang, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25468 · Cited by 18 · see citing works
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning
Qu, Q., Shen, Y., Chen, X., Chung, Y. Y., & Liu, T. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i5.28263 · Cited by 18 · see citing works
Asymmetric Event-Guided Video Super-Resolution
Xiao, Z., Kai, D., Zhang, Y., Sun, X., & Xiong, Z. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681357 · Cited by 18 · see citing works
L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras
Ta, K., Brüggemann, D., Brödermann, T., Sakaridis, C., & Gool, L. V. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161220 · Cited by 17 · see citing works
Ahmad, S., Morerio, P., & Del Bue, A. (2024) · IEEE Access · doi:10.1109/access.2024.3399539 · Cited by 16 · see citing works
Faces in Event Streams (FES): An Annotated Face Dataset for Event Cameras
Bissarinova, U., Rakhimzhanova, T., Kenzhebalin, D., & Varol, H. A. (2024) · Sensors · doi:10.3390/s24051409 · Cited by 16 · see citing works
Joint Demosaicing and Denoising for Spike Camera
Dong, Y., Xiong, R., Zhao, J., Zhang, J., Xiaopeng Fan, B., Zhu, S., & Huang, T. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27924 · Cited by 14 · see citing works
High-temporal-resolution event-based vehicle detection and tracking
El-Shair, Z. A., & Rawashdeh, S. A. (2022) · Optical Engineering · doi:10.1117/1.oe.62.3.031209 · Cited by 14 · see citing works
Self-Supervised Joint Dynamic Scene Reconstruction and Optical Flow Estimation for Spiking Camera
Chen, S., Yu, Z., & Huang, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i1.25108 · Cited by 13 · see citing works
STCA-SNN: self-attention-based temporal-channel joint attention for spiking neural networks
Wu, X., Song, Y., Zhou, Y., Jiang, Y., Bai, Y., Li, X., & Yang, X. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1261543 · Cited by 13 · see citing works
Data‐Driven Technology in Event‐Based Vision
Sun, R., Shi, D., Zhang, Y., Li, R., & Li, R. (2021) · Complexity · doi:10.1155/2021/6689337 · Cited by 13 · see citing works
Improved Event-Based Dense Depth Estimation via Optical Flow Compensation
Shi, D., Jing, L., Li, R., Liu, Z., Wang, L., Xu, H., & Zhang, Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160605 · Cited by 11 · see citing works
Motion-aware Latent Diffusion Models for Video Frame Interpolation
Huang, Z., Yu, Y., Yang, L., Qin, C., Zheng, B., Zheng, X., Zhou, Z., Wang, Y., & Yang, W. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680846 · Cited by 11 · see citing works
A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning
Deng, Y., Chen, H., & Li, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i2.27914 · Cited by 10 · see citing works
Towards High-Speed Passive Visible Light Communication with Event Cameras and Digital Micro-Mirrors
Wang, Y., Shen, Y., Xu, K., Hassan, M., Zhao, G., Xu, C., & Hu, W. S. (2024) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/3666025.3699368 · Cited by 9 · see citing works
Neuromorphic Event Signal-Driven Network for Video De-raining
Ge, C., Fu, X., He, P., Wang, K., Cao, C., & Zha, Z.-J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.27957 · Cited by 7 · see citing works
Lossy Compression for Integrating Event Cameras
Freeman, A. C., & Mayer-Patel, K. D. (2021) · DCC · doi:10.1109/dcc50243.2021.00013 · Cited by 6 · see citing works
An Asynchronous Intensity Representation for Framed and Event Video Sources
Freeman, A. C., Singh, M., & Mayer-Patel, K. D. (2023) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3587819.3590969 · Cited by 6 · see citing works
Event-ID: Intrinsic Decomposition Using an Event Camera
Chen, Z., Lu, Z., Ma, D., Tang, H., Jiang, X., Zheng, Q., & Pan, G. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681133 · Cited by 5 · see citing works
Integrating Event Camera Sensor Emulator
Freeman, A. C., & Mayer-Patel, K. D. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3414394 · Cited by 4 · see citing works
RGB-D Visual Perception for Occluded Scenes via Event Camera
Li, S., Wu, Z., Li, Y., Xue, Z., Liu, Y.-S., & Gao, Y. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02438-y · Cited by 3 · see citing works
Turbulence mitigation in neuromorphic camera imagery
Boehrer, N., Nieuwenhuizen, R. P. J., & Dijk, J. (2020) · Emerging Imaging and Sensing Technologies for Security and Defence; and Advanced Manufacturing Technologies for Micro- and Nanosystems in Security and Defence · doi:10.1117/12.2573464 · Cited by 3 · see citing works
RetinoSim: an Event-based Data Synthesis Tool for Neuromorphic Vision Architecture Exploration
Sengupta, J. P., Liu, S., & Andreou, A. G. (2022) · International Conference on Neuromorphic Systems (ICONS) · doi:10.1145/3546790.3546805 · Cited by 3 · see citing works
LLM-EvRep: Learning an LLM-Compatible Event Representation Using a Self-Supervised Framework
Yu, Z., Qu, Q., Zhang, Q., Nan, Z., & Chen, X. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717548 · Cited by 3 · see citing works
Differential Evolution-based Approach for Tone-Mapping of High Dynamic Range Images
Almaghthawi, A., Bourennani, F., & Rasool, I. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110745 · Cited by 3 · see citing works
Dynamic range and precision of hybrid vision sensors
Yang, M., Douglas, R. J., Li, C.-H., Suess, A., Ryu, H., Posch, C., Chen, S., Berner, R., Yagi, T., Fowler, B. A., et al. (2025) · Nature · doi:10.1038/s41586-025-08991-3 · Cited by 3 · see citing works
Single-shot HDR using conventional image sensor shutter functions and optical randomization
Dai, X., Yanny, K., Monakhova, K., & Antipa, N. (2025) · ACM Transactions on Graphics · doi:10.1145/3748718 · Cited by 2 · see citing works
Color4E: Event Demosaicing for Full-color Event Guided Image Deblurring
Ma, Y., Duan, P., Hong, Y., Zhou, C., Zhang, Y., Ren, J., & Shi, B. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681051 · Cited by 2 · see citing works
Temporal coding enables hyperacuity in event-based vision
Assa, E., Rivkind, A., Kreisserman, M., Khan, F. S., Khan, S. H., & Ahissar, E. (2026) · Nature Communications · doi:10.1038/s41467-026-76878-6 · Cited by 2 · see citing works
Brain Inspired Computing: A Systematic Survey and Future TrendsPreprint
Li, G., Deng, L., Tang, H., Pan, G., Tian, Y., Roy, K., & Maass, W. (2023) · doi:10.36227/techrxiv.21837027 · Cited by 2 · see citing works
E2SIFT: Neuromorphic SIFT via Direct Feature Pyramid Recovery from Events
Henry, C., Maharjan, P., Zhu Li, B., & York, G. W. P. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647465 · Cited by 1 · see citing works
Asynchronous Event Processing with Local-Shift Graph Convolutional Network
Sun, L., Zhang, Y., Cheng, J., & Lu, H. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25336 · Cited by 1 · see citing works
Space-time event clouds-based event processing
Wang, Q. (2021) · doi:10.32657/10356/151982
Shi, W., & Liu, L. (2021) · Springer eBooks · doi:10.1007/978-3-030-81564-6_5
Traffic sign detection and recognition using event camera image reconstructionPreprint
Jeziorek, K., & Kryjak, T. (2022) · doi:10.36227/techrxiv.21739610.v1
Faces in Event Streams (FES): An Annotated Face Dataset for Event CamerasPreprint
Bissarinova, U., Rakhimzhanova, T., Kenzhebalin, D., & Varol, H. A. (2023) · doi:10.36227/techrxiv.22826654
6-DOF Pose Estimation For Event Cameras Using A Transformer-Based Approach
Evry, U., Tabia, A., Bonardi, F., Evry, U., Bouchaa, S., & Evry, U. (2025) · Computer Science Research Notes · doi:10.24132/csrn.2025-13
Optical flow constraint equation based on edge-line translation of event camera data
Eda, T., & Kameda, Y. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.4.043024
ECRNet: An Event-Driven Network for Space Target Component Recognition
Yongqi, M. U., Zhi, X., Li, P., Gong, J., Jiang, S., Hu, J., Shi, T., Zhang, P., & Bao, G. (2026) · Springer proceedings in physics · doi:10.1007/978-981-92-4349-5_41
- Authors
- Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D.
- Affiliations
- University of Zurich (CH)
- Intel (United States) (US)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021-06-01
- Volume
- 43
- Issue
- 6
- Pages
- 1964-1980
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2952699404
- Cited by
- 50
- References
- 68
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 11 · 2023: 11 · 2022: 3 · 2021: 10 · 2020: 5
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.zora.uzh.ch
- Licence
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Zurich Open Repository and Archive (University of Zurich)
- TIB Data Manager
- Funders
- Swiss National Center of Competence Research Robotics
- SNSF-ERC
- Qualcomm
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- event cameras, video reconstruction, recurrent neural networks, perceptual loss, natural image statistics, high-speed video, high dynamic range imaging, visual-inertial odometry, object classification
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
