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In this work, we propose a novel transformation for events from an event camera that is equivariant to optical flow under convolutions in the 3-D spatiotemporal domain.
Relationship between integral transform invariances and lie group theory
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The design and use of steerable filters
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The canonical coordinates method for pattern deformation: Theoretical and computational considerations
Segman, J., Rubinstein, J., and Zeevi, Y. Y · 1992
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Shiftable multiscale transforms
Simoncelli, E. P., Freeman, W. T., Adelson, E. H., and Heeger, D. J · 1992
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Design of multi-parameter steerable functions using cascade basis reduction
Teo, P. C. and Hel-Or, Y · 1998
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Event-based visual flow
Benosman, R., Clercq, C., Lagorce, X., Ieng, S.-H., and Bartolozzi, C · 2014
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Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al · 2015
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Converting static image datasets to spiking neuromorphic datasets using saccades
Orchard, G., Jayawant, A., Cohen, G. K., and Thakor, N · 2015
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Steering a predator robot using a mixed frame/event-driven convolutional neural network
Moeys, D. P., Corradi, F., Kerr, E., Vance, P., Das, G., Neil, D., Kerr, D., and Delbrück, T · 2016
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Harmonic networks: Deep translation and rotation equivariance
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2016
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Dynamic steerable blocks in deep residual networks
Jacobsen, J.-H., Brabandere, B. d., and Smeulders, A. W. M · 2017
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Hots: a hierarchy of event-based time-surfaces for pattern recognition
Lagorce, X., Orchard, G., Galluppi, F., Shi, B. E., and Benosman, R. B · 2017
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Cifar10-dvs: an event-stream dataset for object classification
Li, H., Liu, H., Ji, X., Li, G., and Shi, L · 2017
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The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and slam
Mueggler, E., Rebecq, H., Gallego, G., Delbruck, T., and Scaramuzza, D · 2017
Cited alongside, same era.
Cohen, T. S., Geiger, M., Köhler, J., and Welling, M · 2018
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Towards event-driven object detection with off-the-shelf deep learning
Iacono, M., Weber, S., Glover, A., and Bartolozzi, C · 2018
Later among the works it cites.
Iyer, L. R., Chua, Y., and Li, H · 2018
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Event-based vision meets deep learning on steering prediction for self-driving cars
Maqueda, A. I., Loquercio, A., Gallego, G., García, N., and Scaramuzza, D · 2018
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Constant velocity 3d convolution
Sekikawa, Y., Ishikawa, K., Hara, K., Yoshida, Y., Suzuki, K., Sato, I., and Saito, H · 2018
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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Event-based feature tracking with probabilistic data association
Zhu, A. Z., Atanasov, N., and Daniilidis, K · 2017
Cited alongside, same era.
Ev-segnet: Semantic segmentation for event-based cameras
Alonso, I. and Murillo, A. C · 2018
Cited alongside, same era.
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
Cited in the paper.
Cohen, T. S. and Welling, M
Cited in the paper.
Group equivariant convolutional networks
Cohen, T. S. and Welling, M
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Learning so (3) equivariant representations with spherical cnns
Esteves, C., Allen-Blanchette, C., Makadia, A., and Daniilidis, K
Cited in the paper.
Ye, C., Mitrokhin, A., Parameshwara, C., Fermüller, C., Yorke, J. A., and Aloimonos, Y · 2018
Later among the works it cites.
Ev-flownet: Self-supervised optical flow estimation for event-based cameras
Zhu, A., Yuan, L., Chaney, K., and Daniilidis, K · 2018
Later among the works it cites.
Space-time event clouds for gesture recognition: from rgb cameras to event cameras
Wang, Q., Zhang, Y., Yuan, J., and Lu, Y · 2019
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