From a perspective of feature matching, optical flow estimation for event cameras involves identifying event correspondences by comparing feature similarity across accompanying event frames. In this work, we introduces an effective and robust high-dimensional (HD) feature descriptor for event frames, utilizing Vector Symbolic Architectures (VSA). The topological similarity among neighboring variables within VSA contributes to the enhanced representation similarity of feature descriptors for flow-matching points, while its structured symbolic representation capacity facilitates feature fusion from both event polarities and multiple spatial scales. Based on this HD feature descriptor, we propose a novel feature matching framework for event-based optical flow, encompassing both model-based (VSA-Flow) and self-supervised learning (VSA-SM) methods. In VSA-Flow, accurate optical flow estimation validates the effectiveness of HD feature descriptors. In VSA-SM, a novel similarity maximization method based on the HD feature descriptor is proposed to learn optical flow in a self-supervised way from events alone, eliminating the need for auxiliary grayscale images. Evaluation results demonstrate that our VSA-based method achieves superior accuracy in comparison to both model-based and self-supervised learning methods on the DSEC benchmark, while remains competitive among both methods on the MVSEC benchmark. This contribution marks a significant advancement in event-based optical flow within the feature matching methodology.

翻译：从特征匹配的角度来看，事件相机的光流估计需要通过比较相邻事件帧的特征相似度来识别事件对应关系。本文引入了一种基于向量符号架构（VSA）的有效且鲁棒的高维（HD）事件帧特征描述子。VSA中相邻变量间的拓扑相似性增强了流匹配点特征描述子的表示相似性，而其结构化的符号表示能力则促进了来自事件极性和多空间尺度的特征融合。基于该高维特征描述子，我们提出了一种新颖的事件驱动光流特征匹配框架，涵盖基于模型的方法（VSA-Flow）和自监督学习方法（VSA-SM）。在VSA-Flow中，精确的光流估计验证了高维特征描述子的有效性。在VSA-SM中，我们提出了一种基于高维特征描述子的新型相似度最大化方法，仅通过事件数据以自监督方式学习光流，无需辅助灰度图像。评估结果表明，在DSEC基准上，我们的基于VSA的方法相较于基于模型和自监督学习方法均取得了更优的精度，同时在MVSEC基准上两种方法中均保持竞争力。这一贡献标志着特征匹配方法在事件驱动光流领域的重要进展。