Spike-Based Optical Flow Estimation Via Contrastive Learning

Spiking cameras have shown promising advantages for optical flow estimation in high-speed scenarios. The recent work SCFlow [1] attempts to train an optical flow model using spike frames based on a multi-scale flow reconstruction loss. However, only using the flow reconstruction loss is unable to effectively deal with the details of motion, which may lead to noise and blur in the estimated flow fields. To address this issue, we introduce a contrastive loss into spike-based optical flow estimation, which exploits both the information of positive samples and negative samples. Moreover, we propose a refinement step with flexible reception fields to effectively refine the initial flow fields. Experiments on the spiking optical flow dataset PHM demonstrate that the proposed network is effective for spike-based optical flow estimation. In addition, our method achieves competitive performance compared to recent spike-based, frame-based, and event-based methods.