Event Camera Calibration,
2021
Online
WWW Link. And the workshop paper to appear.
PDF File.
2106
Toolbox to facilitate event camera calibration.
The framework uses neural network-based image reconstruction to enable
compatibility with any existing calibration toolbox.
BibRef
Barranco, F.[Francisco],
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Contour Motion Estimation for Asynchronous Event-Driven Cameras,
PIEEE(102), No. 10, October 2014, pp. 1537-1556.
IEEE DOI
1410
computer vision
BibRef
Barranco, F.[Francisco],
Teo, C.L.,
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Contour Detection and Characterization for Asynchronous Event Sensors,
ICCV15(486-494)
IEEE DOI
1602
Computer vision
BibRef
Liu, H.C.[Han-Chao],
Zhang, F.L.[Fang-Lue],
Marshall, D.[David],
Shi, L.P.[Lu-Ping],
Hu, S.M.[Shi-Min],
High-speed video generation with an event camera,
VC(33), No. 6-8, June 2017, pp. 749-759.
Springer DOI
1706
Only record events when the light on a pixel changes. Good for high speed
images, but incomplete data.
BibRef
Munda, G.[Gottfried],
Reinbacher, C.[Christian],
Pock, T.[Thomas],
Real-Time Intensity-Image Reconstruction for Event Cameras Using
Manifold Regularisation,
IJCV(126), No. 12, December 2018, pp. 1381-1393.
Springer DOI
1811
BibRef
Reinbacher, C.[Christian],
Graber, G.[Gottfried],
Pock, T.[Thomas],
Real-Time Intensity-Image Reconstruction for Event Cameras Using
Manifold Regularisation,
BMVC16(xx-yy).
DOI Link
1805
From per-pixel intensity changes, not intensity level.
The inverse of change detection.
BibRef
Gallego, G.[Guillermo],
Lund, J.E.A.[Jon E.A.],
Mueggler, E.[Elias],
Rebecq, H.[Henri],
Delbruck, T.[Tobi],
Scaramuzza, D.[Davide],
Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps,
PAMI(40), No. 10, October 2018, pp. 2402-2412.
IEEE DOI
1809
Cameras, Standards, Tracking, Voltage control, Robot vision systems,
Event-based vision, pose tracking, dynamic vision sensor,
AR/VR
BibRef
Rebecq, H.[Henri],
Gallego, G.[Guillermo],
Scaramuzza, D.[Davide],
EMVS: Event-based Multi-View Stereo,
BMVC16(xx-yy).
HTML Version.
1805
Stereo with event (pixel change, not value) cameras.
BibRef
Rebecq, H.[Henri],
Ranftl, R.[René],
Koltun, V.[Vladlen],
Scaramuzza, D.[Davide],
High Speed and High Dynamic Range Video with an Event Camera,
PAMI(43), No. 6, June 2021, pp. 1964-1980.
IEEE DOI
2106
BibRef
Earlier:
Events-To-Video: Bringing Modern Computer Vision to Event Cameras,
CVPR19(3852-3861).
IEEE DOI
2002
Code, HDR.
Dataset, HDR.
Dataset, E2VID.
HTML Version. Image reconstruction, Cameras, Streaming media, Dynamic range,
Brightness, Heuristic algorithms,
high dynamic range
BibRef
Rebecq, H.[Henri],
Gallego, G.[Guillermo],
Mueggler, E.[Elias],
Scaramuzza, D.[Davide],
EMVS: Event-Based Multi-View Stereo: 3D Reconstruction with an Event
Camera in Real-Time,
IJCV(126), No. 12, December 2018, pp. 1394-1414.
Springer DOI
1811
BibRef
Peng, X.[Xin],
Wang, Y.[Yifu],
Gao, L.[Ling],
Kneip, L.[Laurent],
Globally-optimal Event Camera Motion Estimation,
ECCV20(XXVI:51-67).
Springer DOI
2011
BibRef
Zhou, Y.[Yi],
Gallego, G.[Guillermo],
Rebecq, H.[Henri],
Kneip, L.[Laurent],
Li, H.D.[Hong-Dong],
Scaramuzza, D.[Davide],
Semi-dense 3D Reconstruction with a Stereo Event Camera,
ECCV18(I: 242-258).
Springer DOI
1810
BibRef
Zebhi, S.[Saeedeh],
Al-Modarresi, S.M.T.,
Abootalebi, V.[Vahid],
Converting video classification problem to image classification with
global descriptors and pre-trained network,
IET-CV(14), No. 8, December 2020, pp. 614-624.
DOI Link
2012
Use a motion history image.
BibRef
Cadena, P.R.G.,
Qian, Y.,
Wang, C.,
Yang, M.,
SPADE-E2VID: Spatially-Adaptive Denormalization for Event-Based Video
Reconstruction,
IP(30), 2021, pp. 2488-2500.
IEEE DOI
2102
Image reconstruction, Cameras, Training, Image resolution,
Task analysis, Optical losses, Brightness, Image reconstruction,
sparse image
BibRef
Peveri, F.[Francesca],
Testa, S.[Simone],
Sabatini, S.P.[Silvio P.],
A Cortically-inspired Architecture for Event-based Visual Motion
Processing: From Design Principles to Real-world Applications,
EventVision21(1395-1402)
IEEE DOI
2109
Visualization, Neuromorphics, Neurons, Neural networks,
Computer architecture, Detectors, Spatial filters
BibRef
Nunes, U.M.[Urbano Miguel],
Demiris, Y.[Yiannis],
Live Demonstration: Incremental Motion Estimation for Event-based
Cameras by Dispersion Minimisation,
EventVision21(1322-1323)
IEEE DOI
2109
Portable computers, Motion estimation, Cameras,
Minimization, Pattern recognition, Motion measurement
BibRef
Delbruck, T.[Tobi],
Graca, R.[Rui],
Paluch, M.[Marcin],
Feedback control of event cameras,
EventVision21(1324-1332)
IEEE DOI
2109
Current measurement, Bandwidth,
Production, Vision sensors, Cameras
BibRef
Duwek, H.C.[Hadar Cohen],
Shalumov, A.[Albert],
Tsur, E.E.[Elishai Ezra],
Image Reconstruction from Neuromorphic Event Cameras using
Laplacian-Prediction and Poisson Integration with Spiking and
Artificial Neural Networks,
EventVision21(1333-1341)
IEEE DOI
2109
Visualization, Laplace equations, Neuromorphics, Pipelines, Cameras,
Sensors, Pattern recognition
BibRef
Jiao, J.H.[Jian-Hao],
Huang, H.Y.[Huai-Yang],
Li, L.[Liang],
He, Z.J.[Zhi-Jian],
Zhu, Y.L.[Yi-Long],
Liu, M.[Ming],
Comparing Representations in Tracking for Event Camera-based SLAM,
EventVision21(1369-1376)
IEEE DOI
2109
Tracking loops, Simultaneous localization and mapping, Tracking, Trajectory
BibRef
Nehvi, J.[Jalees],
Golyanik, V.[Vladislav],
Mueller, F.[Franziska],
Seidel, H.P.[Hans-Peter],
Elgharib, M.[Mohamed],
Theobalt, C.[Christian],
Differentiable Event Stream Simulator for Non-Rigid 3D Tracking,
EventVision21(1302-1311)
IEEE DOI
2109
Training, Surface reconstruction,
Supervised learning, Gesture recognition, Trajectory
BibRef
Muglikar, M.[Manasi],
Gehrig, M.[Mathias],
Gehrig, D.[Daniel],
Scaramuzza, D.[Davide],
How to Calibrate Your Event Camera,
EventVision21(1403-1409)
IEEE DOI
2109
Computational modeling, Robot vision systems, Cameras, Distortion,
Calibration, Sensors, Pattern recognition
BibRef
Zhang, L.[Limeng],
Zhang, H.G.[Hong-Guang],
Zhu, C.Y.[Chen-Yang],
Guo, S.[Shasha],
Chen, J.[Jihua],
Wang, L.[Lei],
Fine-grained Video Deblurring with Event Camera,
MMMod21(I:352-364).
Springer DOI
2106
BibRef
Kostadinov, D.[Dimche],
Scaramuzza, D.[Davide],
Unsupervised Feature Learning for Event Data:
Direct vs. Inverse Problem Formulation,
ICPR21(5981-5987)
IEEE DOI
2105
Inverse problems, Computer architecture, Dynamic range, Cameras,
Encoding, Object recognition
BibRef
Zhao, J.,
Xiong, R.,
Zhao, R.,
Wang, J.,
Ma, S.,
Huang, T.,
Motion Estimation for Spike Camera Data Sequence via Spike Interval
Analysis,
VCIP20(371-374)
IEEE DOI
2102
Cameras, Motion estimation, Trajectory, Image reconstruction,
Data models, Estimation, Dynamics, motion analysis,
motion estimation
BibRef
Zhang, S.[Song],
Zhang, Y.[Yu],
Jiang, Z.[Zhe],
Zou, D.Q.[Dong-Qing],
Ren, J.[Jimmy],
Zhou, B.[Bin],
Learning to See in the Dark with Events,
ECCV20(XVIII:666-682).
Springer DOI
2012
BibRef
Wang, B.S.[Bi-Shan],
He, J.W.[Jing-Wei],
Yu, L.[Lei],
Xia, G.S.[Gui-Song],
Yang, W.[Wen],
Event Enhanced High-quality Image Recovery,
ECCV20(XIII:155-171).
Springer DOI
2011
BibRef
Stoffregen, T.[Timo],
Scheerlinck, C.[Cedric],
Scaramuzza, D.[Davide],
Drummond, T.[Tom],
Barnes, N.[Nick],
Kleeman, L.[Lindsay],
Mahony, R.[Robert],
Reducing the Sim-to-real Gap for Event Cameras,
ECCV20(XXVII:534-549).
Springer DOI
2011
BibRef
Harrigan, S.,
Coleman, S.,
Kerr, D.,
Yogarajah, P.,
Fang, Z.,
Wu, C.,
Post-Stimulus Time-Dependent Event Descriptor,
ICIP20(385-389)
IEEE DOI
2011
Support vector machines, Vision sensors, Lattices,
Feature extraction, Machine learning,
Computer Vision
BibRef
Su, B.,
Yu, L.,
Yang, W.,
Event-Based High Frame-Rate Video Reconstruction With A Novel
Cycle-Event Network,
ICIP20(86-90)
IEEE DOI
2011
Image reconstruction, Cameras, Generators, Logic gates, Training,
Generative adversarial networks, Streaming media, Event camera,
GAN
BibRef
Jiang, M.,
Liu, Z.,
Wang, B.,
Yu, L.,
Yang, W.,
Robust Intensity Image Reconstruciton Based On Event Cameras,
ICIP20(968-972)
IEEE DOI
2011
Cameras, Image reconstruction, Streaming media,
Reconstruction algorithms, Mathematical model, Brightness,
Motion blur
BibRef
Liu, D.,
Parra, Á.,
Chin, T.,
Globally Optimal Contrast Maximisation for Event-Based Motion
Estimation,
CVPR20(6348-6357)
IEEE DOI
2008
Upper bound, Estimation, Cameras, Streaming media,
Robot vision systems, Motion estimation, Kernel
BibRef
Gehrig, D.,
Gehrig, M.,
Hidalgo-Carrió, J.,
Scaramuzza, D.,
Video to Events: Recycling Video Datasets for Event Cameras,
CVPR20(3583-3592)
IEEE DOI
PDF File.
2008
Code, Event Camera.
WWW Link. ESIM: Event camera simulator:
WWW Link. Video:
WWW Link. Cameras, Sensors, Semantics, Standards, Brightness, Task analysis,
Machine learning
BibRef
Baldwin, R.W.[R. Wes],
Almatrafi, M.[Mohammed],
Asari, V.[Vijayan],
Hirakawa, K.[Keigo],
Event Probability Mask (EPM) and Event Denoising Convolutional Neural
Network (EDnCNN) for Neuromorphic Cameras,
CVPR20(1698-1707)
IEEE DOI
2008
Cameras, Voltage control, Noise reduction, Neuromorphics, Hardware,
Benchmark testing, Noise measurement
BibRef
Tulyakov, S.[Stepan],
Fleuret, F.[Francois],
Kiefel, M.[Martin],
Gehler, P.[Peter],
Hirsch, M.[Michael],
Learning an Event Sequence Embedding for Dense Event-Based Deep
Stereo,
ICCV19(1527-1537)
IEEE DOI
2004
Event camera.
biomimetics, cameras, image representation,
image sensors, learning (artificial intelligence), Power demand
BibRef
Wang, Q.Y.[Qin-Yi],
Zhang, Y.X.[Ye-Xin],
Yuan, J.S.[Jun-Song],
Lu, Y.L.[Yi-Long],
Space-Time Event Clouds for Gesture Recognition:
From RGB Cameras to Event Cameras,
WACV19(1826-1835)
IEEE DOI
1904
Sense motion, event streams. space-time location of intensity change.
cameras, gesture recognition, image motion analysis, image sensors,
neural net architecture, individual event, space-time location,
Real-time systems
BibRef
Alzugaray, I.,
Chli, M.,
Asynchronous Multi-Hypothesis Tracking of Features with Event Cameras,
3DV19(269-278)
IEEE DOI
1911
BibRef
Earlier:
ACE: An Efficient Asynchronous Corner Tracker for Event Cameras,
3DV18(653-661)
IEEE DOI
1812
Tracking, Cameras, Feature extraction, Visualization,
Streaming media, Robot vision systems, SLAM, dvs, visual odometry,
visual tracking.
image motion analysis, image sequences,
efficient asynchronous corner tracker.
BibRef
Pan, L.Y.[Li-Yuan],
Scheerlinck, C.[Cedric],
Yu, X.[Xin],
Hartley, R.[Richard],
Liu, M.M.[Miao-Miao],
Dai, Y.[Yuchao],
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera,
CVPR19(6813-6822).
IEEE DOI
2002
BibRef
Stoffregen, T.[Timo],
Kleeman, L.[Lindsay],
Event Cameras, Contrast Maximization and Reward Functions: An Analysis,
CVPR19(12292-12300).
IEEE DOI
2002
Event cameras asynchronously report timestamped changes in pixel intensity.
BibRef
Scheerlinck, C.[Cedric],
Barnes, N.[Nick],
Mahony, R.[Robert],
Continuous-Time Intensity Estimation Using Event Cameras,
ACCV18(V:308-324).
Springer DOI
1906
Asynchronous, data-driven measurements of local temporal contrast.
BibRef
Gehrig, D.[Daniel],
Loquercio, A.[Antonio],
Derpanis, K.[Konstantinos],
Scaramuzza, D.[Davide],
End-to-End Learning of Representations for Asynchronous Event-Based
Data,
ICCV19(5632-5642)
IEEE DOI
2004
Event camers: pixel changes.
cameras, computer vision, convolutional neural nets,
image motion analysis, image representation, image sensors,
Spatiotemporal phenomena
BibRef
Gao, S.,
Guo, G.,
Chen, C.L.P.[C. L. Philip],
Event-Based Incremental Broad Learning System for Object
Classification,
CEFRL19(2989-2998)
IEEE DOI
2004
cameras, convolutional neural nets, image classification,
image sensors, learning (artificial intelligence),
event camera
BibRef
Gallego, G.,
Rebecq, H.,
Scaramuzza, D.,
A Unifying Contrast Maximization Framework for Event Cameras, with
Applications to Motion, Depth, and Optical Flow Estimation,
CVPR18(3867-3876)
IEEE DOI
1812
Trajectory, Estimation, Cameras, Optical imaging, Brightness,
Image edge detection, Computer vision
BibRef
Barua, S.,
Miyatani, Y.,
Veeraraghavan, A.,
Direct face detection and video reconstruction from event cameras,
WACV16(1-9)
IEEE DOI
1606
Cameras
BibRef
Kim, H.[Hanme],
Leutenegger, S.[Stefan],
Davison, A.J.[Andrew J.],
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera,
ECCV16(VI: 349-364).
Springer DOI
1611
BibRef
Kim, H.[Hanme],
Handa, A.[Ankur],
Benosman, R.[Ryad],
Ieng, S.H.[Sio-Hoi],
Davison, A.J.[Andrew J.],
Simultaneous Mosaicing and Tracking with an Event Camera,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Gobron, S.[Stéphane],
Ahn, J.H.[Jung-Hyun],
Garcia, D.[David],
Silvestre, Q.[Quentin],
Thalmann, D.[Daniel],
Boulic, R.[Ronan],
An Event-Based Architecture to Manage Virtual Human Non-Verbal
Communication in 3D Chatting Environment,
AMDO12(58-68).
Springer DOI
1208
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Differencing Papers -- Ramesh Jain .