发表机构
Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文将事件相机特征视为运动线索，分析了结构张量特征值与时空密度值，结合局部几何特征增强运动估计，在DSEC基准上提升了光流网络的准确性，数据稀缺与低容量模型增益显著。
AI 中文摘要
事件相机以高时间分辨率异步捕捉强度变化，这需要为下游任务开发新颖的预处理方法。与静态的强度快照不同，事件数据固有地编码了场景动态和物体运动的信息，这意味着从事件中衍生的特征可能表现出与基于帧的视觉没有直接相似性的行为。在本文中，我们分析了基于事件的角点检测中使用的两个特征——结构张量的特征值和时空密度值——并证明它们是运动线索。我们假设这些特征结合局部几何信息可以增强运动估计任务。为了验证这一点，我们首先从理论上分析了运动角点处结构张量的特征值与运动方向的关系。然后，我们在合成数据集上设计了受控实验，证实用特征值和密度值扩展局部几何特征可提供互补的运动信息，且对纹理和散粒噪声具有鲁棒性。最后，我们将所提出的特征集成到最先进的基于事件的光流网络中，并在真实世界的DSEC基准上进行评估，结果显示添加的特征始终提高了准确性，在数据稀缺场景和低容量模型中获得的增益最大。本文的代码可在此处获取：this https URL。
英文摘要
Event cameras capture intensity changes asynchronously with high temporal resolution, requiring novel preprocessing methods for downstream tasks. Unlike static intensity snapshots, event data inherently encode information about scene dynamics and object motion, meaning that features derived from events can exhibit behaviors with no direct analogue in frame-based vision. In this paper, we analyze two features used in event-based corner detection---the eigenvalues of the structure tensor and the spatiotemporal density values---and show that they are \emph{motion cues}. We hypothesize that these features, combined with local geometric information, can enhance motion estimation tasks. To validate this, we first theoretically analyze how the eigenvalues of the structure tensor at moving corner points relate to the direction of motion. We then design controlled experiments on a synthetic dataset, confirming that extending local geometric features with eigenvalues and density values provides complementary motion information and is robust to texture and shot noise. Finally, we integrate the proposed features into a state-of-the-art event-based optical flow network and evaluate on the real-world DSEC benchmark, where the added features consistently improve accuracy, with the largest gains in data-scarce scenarios and for lower-capacity models. The code for this paper can be found at: \href{https://github.com/hesamaraghi/static-in-frames-dynamic-in-events}{https://github.com/hesamaraghi/static-in-frames-dynamic-in-events}.
