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Learning Optical Flow from Continuous Spike Streams
Rui Zhao · Ruiqin Xiong · Jing Zhao · Zhaofei Yu · Xiaopeng Fan · Tiejun Huang


Spike camera is an emerging bio-inspired vision sensor with ultra-high temporal resolution. It records scenes by accumulating photons and outputting continuous binary spike streams. Optical flow is a key task for spike cameras and their applications. A previous attempt has been made for spike-based optical flow. However, the previous work only focuses on motion between two moments, and it uses graphics-based data for training, whose generalization is limited. In this paper, we propose a tailored network, Spike2Flow that extracts information from binary spikes with temporal-spatial representation based on the differential of spike firing time and spatial information aggregation. The network utilizes continuous motion clues through joint correlation decoding. Besides, a new dataset with real-world scenes is proposed for better generalization. Experimental results show that our approach achieves state-of-the-art performance on existing synthetic datasets and real data captured by spike cameras. The source code and dataset are available at \url{https://github.com/ruizhao26/Spike2Flow}.

Author Information

Rui Zhao (Peking University)
Ruiqin Xiong (Peking University)
Jing Zhao
Zhaofei Yu (Peking University)
Xiaopeng Fan (Harbin Institute of Technology)
Tiejun Huang (Peking University)

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