Event cameras have the potential to capture continuous motion information over time and space, making them well-suited for optical flow estimation. However, most existing learning-based methods for event-based optical flow adopt frame-based techniques, ignoring the spatio-temporal characteristics of events. Additionally, these methods assume linear motion between consecutive events within the loss time window, which increases optical flow errors in long-time sequences. In this work, we observe that rich spatio-temporal information and accurate nonlinear motion between events are crucial for event-based optical flow estimation. Therefore, we propose E-NMSTFlow, a novel unsupervised event-based optical flow network focusing on long-time sequences. We propose a Spatio-Temporal Motion Feature Aware (STMFA) module and an Adaptive Motion Feature Enhancement (AMFE) module, both of which utilize rich spatio-temporal information to learn spatio-temporal data associations. Meanwhile, we propose a nonlinear motion compensation loss that utilizes the accurate nonlinear motion between events to improve the unsupervised learning of our network. Extensive experiments demonstrate the effectiveness and superiority of our method. Remarkably, our method ranks first among unsupervised learning methods on the MVSEC and DSEC-Flow datasets.

Most existing event-based camera pose relocalization (CPR) learning methods implicitly encode environmental information into network parameters to achieve end-to-end mapping from event stream to pose. However, these end-to-end CPR methods fail to utilize prior environmental information effectively. As the scale of the environment increases, the difficulty of this mapping relationship grows significantly, reducing the robustness of the end-to-end methods across different scenarios. To address the above issues, this paper proposes the first coarse-to-fine event-based CPR framework, which achieves a new paradigm from end-to-end pose regression network to a hierarchical approach. In the coarse localization stage, we effectively encode similarity features by incorporating the fine-grained temporal information, achieving accurate retrieval of nearby event stream. In the pose refinement stage, we present an Event Spatio-temporal Pose Refinement Network (ESPR-Net) based on the Recurrent Convolutional Neural Networks (RCNN) architecture, which is capable of learning more nu-anced spatio-temporal features to achieve accurate regression of the relative pose. Finally, we conducted a comprehensive comparison on the IJRR and M3ED dataset, achieving state-of-the-art (SOTA) performance on both. Notably, our method attains a significant 83 % performance improvement on the outdoor M3ED dataset.