Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion

CVPR 2019 Alex Zihao ZhuLiangzhe YuanKenneth ChaneyKostas Daniilidis

In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input representation of the events in the form of a discretized volume that maintains the temporal distribution of the events, which we pass through a neural network to predict the motion of the events... (read more)

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