Event cameras deliver visual information characterized by a high dynamic range and high temporal resolution, offering significant advantages in estimating optical flow for complex lighting conditions and fast-moving objects. Current advanced optical flow methods for event cameras largely adopt established image-based frameworks. However, the spatial sparsity of event data limits their performance. In this paper, we present BAT, an innovative framework that estimates event-based optical flow using bidirectional adaptive temporal correlation. BAT includes three novel designs: 1) a bidirectional temporal correlation that transforms bidirectional temporally dense motion cues into spatially dense ones, enabling accurate and spatially dense optical flow estimation; 2) an adaptive temporal sampling strategy for maintaining temporal consistency in correlation; 3) spatially adaptive temporal motion aggregation to efficiently and adaptively aggregate consistent target motion features into adjacent motion features while suppressing inconsistent ones. Our results rank $1^{st}$ on the DSEC-Flow benchmark, outperforming existing state-of-the-art methods by a large margin while also exhibiting sharp edges and high-quality details. Notably, our BAT can accurately predict future optical flow using only past events, significantly outperforming E-RAFT's warm-start approach. Code: \textcolor{magenta}{https://github.com/gangweiX/BAT}.

翻译：事件相机提供的视觉信息具有高动态范围和高时间分辨率的特点，在复杂光照条件和快速运动物体的光流估计方面具有显著优势。当前先进的事件相机光流方法大多采用成熟的图像处理框架，但事件数据的空间稀疏性限制了其性能。本文提出BAT，一种利用双向自适应时间相关性估计事件光流的创新框架。BAT包含三项新颖设计：1）双向时间相关性模块，将双向时间密集的运动线索转化为空间密集线索，实现精确且空间密集的光流估计；2）自适应时间采样策略，保持相关性的时间一致性；3）空间自适应时间运动聚合机制，高效且自适应地将一致的目标运动特征聚合到相邻运动特征中，同时抑制不一致特征。我们的方法在DSEC-Flow基准测试中排名第一，以显著优势超越现有最优方法，同时展现出清晰的边缘和高质量的细节特征。值得注意的是，BAT仅使用过去事件即可准确预测未来光流，其性能显著优于E-RAFT的热启动方法。代码地址：\textcolor{magenta}{https://github.com/gangweiX/BAT}。