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Event-based cameras are bio-inspired vision sensors whose pixels work independently from each other and respond asynchronously to brightness changes, with microsecond resolution.
D. Marr and T. Poggio, “Cooperative computation of stereo disparity,” Science , vol. 194, no. 4262, pp. 283–287, 1976
1976
Earlier work this paper cites.
P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 14, no. 2, pp. 239–256, 1992
1992
Earlier work this paper cites.
R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision . Cambridge University Press, 2003, 2nd Edition
2003
Earlier work this paper cites.
S. Kotz and S. Nadarajah, Multivariate t-distributions and their applications . Cambridge University Press, 2004
2004
Earlier work this paper cites.
S. Baker and I. Matthews, “Lucas-kanade 20 years on: A unifying framework,” Int. J. Comput. Vis. , vol. 56, no. 3, pp. 221–255, 2004
2004
Earlier work this paper cites.
G. Klein and D. Murray, “Parallel tracking and mapping for small AR workspaces,” in IEEE ACM Int. Sym. Mixed and Augmented Reality (ISMAR) , Nara, Japan, Nov. 2007, pp. 225–234
2007
Earlier work this paper cites.
P. Lichtsteiner, C. Posch, and T. Delbruck, “A 128 × \times 128 120 dB 15 μ \mu s latency asynchronous temporal contrast vision sensor,” IEEE J. Solid-State Circuits , vol. 43, no. 2, pp. 566–576, 2008
2008
Earlier work this paper cites.
H. Hirschmuller, “Stereo processing by semiglobal matching and mutual information,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 30, no. 2, pp. 328–341, Feb. 2008
2008
Earlier work this paper cites.
T. Delbruck, “Frame-free dynamic digital vision,” in Proc. Int. Symp. Secure-Life Electron. , 2008, pp. 21–26
2008
Earlier work this paper cites.
J. Conradt, M. Cook, R. Berner, P. Lichtsteiner, R. J. Douglas, and T. Delbruck, “A pencil balancing robot using a pair of AER dynamic vision sensors,” in IEEE Int. Symp. Circuits Syst. (ISCAS) , 2009, pp. 781–784
2009
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng, “ROS: an open-source Robot Operating System,” in ICRA Workshop Open Source Softw. , vol. 3, no. 2, 2009, p. 5
2009
Earlier work this paper cites.
S.-C. Liu and T. Delbruck, “Neuromorphic sensory systems,” Current Opinion in Neurobiology , vol. 20, no. 3, pp. 288–295, 2010
2010
Earlier work this paper cites.
J. Kogler, M. Humenberger, and C. Sulzbachner, “Event-based stereo matching approaches for frameless address event stereo data,” in Int. Symp. Adv. Vis. Comput. (ISVC) , 2011, pp. 674–685
2011
Earlier work this paper cites.
C. Posch, D. Matolin, and R. Wohlgenannt, “A QVGA 143 dB dynamic range frame-free PWM image sensor with lossless pixel-level video compression and time-domain CDS,” IEEE J. Solid-State Circuits , vol. 46, no. 1, pp. 259–275, Jan. 2011
2011
Earlier work this paper cites.
M. Cook, L. Gugelmann, F. Jug, C. Krautz, and A. Steger, “Interacting maps for fast visual interpretation,” in Int. Joint Conf. Neural Netw. (IJCNN) , 2011, pp. 770–776
2011
Earlier work this paper cites.
R. Benosman, S.-H. Ieng, P. Rogister, and C. Posch, “Asynchronous event-based Hebbian epipolar geometry,” IEEE Trans. Neural Netw. , vol. 22, no. 11, pp. 1723–1734, 2011
2011
Earlier work this paper cites.
R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “DTAM: Dense tracking and mapping in real-time,” in Int. Conf. Comput. Vis. (ICCV) , 2011, pp. 2320–2327
2011
Earlier work this paper cites.
P. Rogister, R. Benosman, S.-H. Ieng, P. Lichtsteiner, and T. Delbruck, “Asynchronous event-based binocular stereo matching,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 23, no. 2, pp. 347–353, 2012
2012
Earlier work this paper cites.
D. Weikersdorfer and J. Conradt, “Event-based particle filtering for robot self-localization,” in IEEE Int. Conf. Robot. Biomimetics (ROBIO) , 2012, pp. 866–870
2012
Earlier work this paper cites.
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of RGB-D SLAM systems,” in IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS) , Oct. 2012
2012
Earlier work this paper cites.
T. Delbruck and M. Lang, “Robotic goalie with 3ms reaction time at 4% CPU load using event-based dynamic vision sensor,” Front. Neurosci. , vol. 7, p. 223, 2013
2013
Earlier work this paper cites.
D. Weikersdorfer, R. Hoffmann, and J. Conradt, “Simultaneous localization and mapping for event-based vision systems,” in Int. Conf. Comput. Vis. Syst. (ICVS) , 2013, pp. 133–142
2013
Earlier work this paper cites.
C. Kerl, J. Sturm, and D. Cremers, “Robust odometry estimation for rgb-d cameras,” in IEEE Int. Conf. Robot. Autom. (ICRA) , 2013
2013
Earlier work this paper cites.
D. Weikersdorfer, D. B. Adrian, D. Cremers, and J. Conradt, “Event-based 3D SLAM with a depth-augmented dynamic vision sensor,” in IEEE Int. Conf. Robot. Autom. (ICRA) , 2014, pp. 359–364
2014
Cited alongside, same era.
A. Censi and D. Scaramuzza, “Low-latency event-based visual odometry,” in IEEE Int. Conf. Robot. Autom. (ICRA) , 2014, pp. 703–710
2014
Cited alongside, same era.
L. A. Camunas-Mesa, T. Serrano-Gotarredona, S. H. Ieng, R. B. Benosman, and B. Linares-Barranco, “On the use of orientation filters for 3D reconstruction in event-driven stereo vision,” Front. Neurosci. , vol. 8, p. 48, 2014
2014
Cited alongside, same era.
E. Piatkowska, A. N. Belbachir, and M. Gelautz, “Cooperative and asynchronous stereo vision for dynamic vision sensors,” Meas. Sci. Technol. , vol. 25, no. 5, p. 055108, Apr. 2014
2014
Cited alongside, same era.
R. Mur-Artal and J. D. Tardós, “ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras,” IEEE Trans. Robot. , vol. 33, no. 5, pp. 1255–1262, Oct. 2017
2017
Later among the works it cites.
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 9 μ \mu m pixel and 300Meps address-event representation,” in IEEE Intl. Solid-State Circuits Conf. (ISSCC) , 2017
2017
Later among the works it cites.
G. Gallego, J. E. A. Lund, E. Mueggler, H. Rebecq, T. Delbruck, and D. Scaramuzza, “Event-based, 6-DOF camera tracking from photometric depth maps,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 40, no. 10, pp. 2402–2412, Oct. 2018
2018
Later among the works it cites.
A. Rosinol Vidal, H. Rebecq, T. Horstschaefer, and D. Scaramuzza, “Ultimate SLAM? combining events, images, and IMU for robust visual SLAM in HDR and high speed scenarios,” IEEE Robot. Autom. Lett. , vol. 3, no. 2, pp. 994–1001, Apr. 2018
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H. Kim, A. Handa, R. Benosman, S.-H. Ieng, and A. J. Davison, “Simultaneous mosaicing and tracking with an event camera,” in British Mach. Vis. Conf. (BMVC) , 2014
2014
Cited alongside, same era.
E. Mueggler, B. Huber, and D. Scaramuzza, “Event-based, 6-DOF pose tracking for high-speed maneuvers,” in IEEE/RSJ Int. Conf. Intell. Robot. Syst. (IROS) , 2014, pp. 2761–2768
2014
Cited alongside, same era.
R. Benosman, C. Clercq, X. Lagorce, S.-H. Ieng, and C. Bartolozzi, “Event-based visual flow,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 25, no. 2, pp. 407–417, 2014
2014
Cited alongside, same era.
J. Engel, J. Schöps, and D. Cremers, “LSD-SLAM: Large-scale direct monocular SLAM,” in Eur. Conf. Comput. Vis. (ECCV) , 2014, pp. 834–849
2014
Cited alongside, same era.
C. Brandli, T. Mantel, M. Hutter, M. Höpflinger, R. Berner, R. Siegwart, and T. Delbruck, “Adaptive pulsed laser line extraction for terrain reconstruction using a dynamic vision sensor,” Front. Neurosci. , vol. 7, p. 275, 2014
2014
Cited alongside, same era.
X. Lagorce, C. Meyer, S.-H. Ieng, D. Filliat, and R. Benosman, “Asynchronous event-based multikernel algorithm for high-speed visual features tracking,” IEEE Trans. Neural Netw. Learn. Syst. , vol. 26, no. 8, pp. 1710–1720, Aug. 2015
2015
Cited alongside, same era.
E. Mueggler, G. Gallego, and D. Scaramuzza, “Continuous-time trajectory estimation for event-based vision sensors,” in Robotics: Science and Systems (RSS) , 2015
2015
Cited alongside, same era.
H. Kim, S. Leutenegger, and A. J. Davison, “Real-time 3D reconstruction and 6-DoF tracking with an event camera,” in Eur. Conf. Comput. Vis. (ECCV) , 2016, pp. 349–364
2016
Cited alongside, same era.
2018
Later among the works it cites.
E. Mueggler, G. Gallego, H. Rebecq, and D. Scaramuzza, “Continuous-time visual-inertial odometry for event cameras,” IEEE Trans. Robot. , vol. 34, no. 6, pp. 1425–1440, Dec. 2018
2018
Later among the works it cites.
Y. Zhou, G. Gallego, H. Rebecq, L. Kneip, H. Li, and D. Scaramuzza, “Semi-dense 3D reconstruction with a stereo event camera,” in Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 242–258
2018
Later among the works it cites.
S.-H. Ieng, J. Carneiro, M. Osswald, and R. Benosman, “Neuromorphic event-based generalized time-based stereovision,” Front. Neurosci. , vol. 12, p. 442, 2018
2018
Later among the works it cites.
H. Rebecq, G. Gallego, E. Mueggler, and D. Scaramuzza, “EMVS: Event-based multi-view stereo—3D reconstruction with an event camera in real-time,” Int. J. Comput. Vis. , vol. 126, no. 12, pp. 1394–1414, Dec. 2018
2018
Later among the works it cites.
G. Gallego, H. Rebecq, and D. Scaramuzza, “A unifying contrast maximization framework for event cameras, with applications to motion, depth, and optical flow estimation,” in IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , 2018, pp. 3867–3876
2018
Later among the works it cites.
A. Z. Zhu, L. Yuan, K. Chaney, and K. Daniilidis, “EV-FlowNet: Self-supervised optical flow estimation for event-based cameras,” in Robotics: Science and Systems (RSS) , 2018
2018
Later among the works it cites.
Y. Zhou, H. Li, and L. Kneip, “Canny-VO: Visual odometry with RGB-D cameras based on geometric 3-D–2-D edge alignment,” IEEE Trans. Robot. , vol. 35, no. 1, pp. 184–199, 2018
2018
Later among the works it cites.
A. Z. Zhu, D. Thakur, T. Ozaslan, B. Pfrommer, V. Kumar, and K. Daniilidis, “The multivehicle stereo event camera dataset: An event camera dataset for 3D perception,” IEEE Robot. Autom. Lett. , vol. 3, no. 3, pp. 2032–2039, Jul. 2018
2018
Later among the works it cites.
A. Z. Zhu, Y. Chen, and K. Daniilidis, “Realtime time synchronized event-based stereo,” in Eur. Conf. Comput. Vis. (ECCV) , 2018, pp. 438–452
2018
Later among the works it cites.
M. Liu and T. Delbruck, “Adaptive time-slice block-matching optical flow algorithm for dynamic vision sensors,” in British Mach. Vis. Conf. (BMVC) , 2018
2018
Later among the works it cites.
S. Bryner, G. Gallego, H. Rebecq, and D. Scaramuzza, “Event-based, direct camera tracking from a photometric 3D map using nonlinear optimization,” in IEEE Int. Conf. Robot. Autom. (ICRA) , 2019, pp. 325–331
2019
Later among the works it cites.
L. Steffen, D. Reichard, J. Weinland, J. Kaiser, A. Rönnau, and R. Dillmann, “Neuromorphic stereo vision: A survey of bio-inspired sensors and algorithms,” Front. Neurorobot. , vol. 13, p. 28, 2019
2019
Later among the works it cites.
G. Gallego, M. Gehrig, and D. Scaramuzza, “Focus is all you need: Loss functions for event-based vision,” in IEEE Conf. Comput. Vis. Pattern Recog. (CVPR) , 2019, pp. 12 272–12 281
2019
Later among the works it cites.
D. Gehrig, A. Loquercio, K. G. Derpanis, and D. Scaramuzza, “End-to-end learning of representations for asynchronous event-based data,” in Int. Conf. Comput. Vis. (ICCV) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
G. Gallego, T. Delbruck, G. Orchard, C. Bartolozzi, B. Taba, A. Censi, S. Leutenegger, A. Davison, J. Conradt, K. Daniilidis, and D. Scaramuzza, “Event-based vision: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , 2020
2020
Closest in time.
D. Gehrig, H. Rebecq, G. Gallego, and D. Scaramuzza, “EKLT: Asynchronous photometric feature tracking using events and frames,” Int. J. Comput. Vis. , vol. 128, pp. 601–618, 2020
2020
Closest in time.
D. Falanga, K. Kleber, and D. Scaramuzza, “Dynamic obstacle avoidance for quadrotors with event cameras,” Science Robotics , vol. 5, no. 40, p. eaaz9712, Mar. 2020
2020
Closest in time.
D. Migliore (Prophesee), “Sensing the world with event-based cameras,” https://robotics.sydney.edu.au/icra-workshop/ , Jun. 2020
2020
Closest in time.