Spike Camera Optical Flow Estimation Based on Continuous Spike Streams.

Abstract

Spike camera is an emerging bio-inspired vision sensor with ultra-high temporal resolution. It records scenes by accumulating photons and outputting binary spike streams. Optical flow estimation aims to estimate pixel-level correspondences between different moments, describing motion information along time, which is a key task of spike camera. High-quality optical flow is important since motion information is a foundation for analyzing spikes. However, extracting stable light-intensity information from spikes is difficult due to the randomness of binary spikes. Besides, the continuity of spikes can offer contextual information for optical flow. In this paper, we propose a network Spike2Flow++ to estimate optical flow for spike camera. In Spike2Flow++, we propose a differential of spike firing time (DSFT) to represent information in binary spikes. Moreover, we propose a dual DSFT representation and a dual correlation construction to extract stable light-intensity information for reliable correlations. To use the continuity of spikes as motion contextual information, we propose a joint correlation decoding (JCD) that jointly estimates a series of flow fields. To adaptively fuse different motions in JCD, we propose a global motion bank aggregation to construct an information bank for all motions and adaptively extract contexts from the bank for each iteration during recurrent decoding of each motion. To train and evaluate our network, we construct a real scene with spikes and flow++ (RSSF++) based on real-world scenes. Experiments demonstrate that our Spike2Flow++ achieves state-of-the-art performance on RSSF++, photo-realistic high-speed motion (PHM), and real-captured data.