发表机构
INSAIT; Sofia University “St. Kliment Ohridski”; Zhejiang University（INSAIT; 索非亚大学“圣·克莱门特·奥赫里德斯基”; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出ODF运动估计方法，结合自适应事件选择与无参数滤波器，在低延迟下实现亚像素精度，其通用性在实时图像去模糊和低功耗跟踪应用中得到验证。
AI 中文摘要
事件驱动运动估计是对时间分辨率要求高且对快速运动具有鲁棒性的任务的核心。现有方法通常依赖迭代优化或重复假设比较，抵消了传感器的低延迟优势。我们提出定向距离场运动估计（ODF Motion Estimation），该方法用对预计算的事件距离向量场进行单次平均步骤取代上述优化，结合自适应事件数量选择策略和无参数轨迹滤波器。在公开及自行收集的数据集上，ODF运动估计达到亚像素精度，且在对比方法中延迟最低。我们在两个下游应用中验证其通用性，而非将其作为独立贡献：其一，估计的轨迹被转换为模糊核，并与紧凑的迭代展开网络配对，该网络在模拟运动估计噪声上训练，用于实时非盲图像去模糊，以不足100万参数达到具有竞争力或更优的PSNR/SSIM；其二，同一预计算场被重新用于低功耗异步瞳孔与光斑跟踪器中的定向事件滤波，维持稳定跟踪数十秒，同时降低近眼模块的功耗。
英文摘要
Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion. Existing methods typically rely on iterative optimization or repeated hypothesis comparison, offsetting the sensor's low-latency advantage. We propose Oriented Distance Field Motion Estimation (ODF Motion Estimation), which replaces this optimization with a single averaging step over a precomputed field of event distance vectors, combined with an adaptive event-count selection strategy and a parameter-free trail filter. On public and self-collected datasets, ODF motion estimation reaches sub-pixel accuracy at the lowest latency among compared methods. We validate its generality on two downstream applications rather than treating them as separate contributions. First, the estimated trajectory is converted into a blur kernel and paired with a compact iterative-unfolding network, trained on simulated motion-estimation noise, for real-time non-blind image deblurring, attaining competitive or superior PSNR/SSIM with under 1M parameters. Second, the same precomputed field is repurposed for directional event filtering in a low-power asynchronous pupil and glint tracker, sustaining stable tracking for tens of seconds while lowering a near-eye module's power draw.
