Event-Based Motion Estimation via Oriented Distance Fields

2026-08-25

AI Summary

本文提出了一种基于方向距离场的运动估计方法，通过预计算事件距离向量并结合自适应事件计数策略和无参数轨迹滤波器，实现了低延迟下的亚像素精度运动估计。

Abstract

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.

Problem

• Event-based motion estimation
• high temporal resolution
• low-latency

Innovation

• Oriented Distance Field
• Event-based Motion Estimation
• Adaptive Event-count Selection
• Parameter-free Trail Filter
• Real-time Non-blind Image Deblurring