用事件数据实现高精度运动分割与自运动估计，适合实时机器人导航。
Motion Segmentation and Egomotion Estimation from Event-Based Normal Flow
- 基于事件数据和法向光流，结合几何约束进行迭代优化。
- 在EVIMO2v2数据集上实现精准分割与平移运动估计，无需完整光流计算。
- 特别擅长处理物体边界，适合低延迟实时系统应用。
本文提出一种针对类脑视觉传感器的鲁棒运动分割与自运动估计框架，利用事件数据的稀疏性与高时间分辨率，结合法向光流、场景结构与惯性测量的几何约束。所提优化流程通过迭代事件过分割、残差分析分离独立运动物体，并基于运动相似性与时间一致性进行分层聚类优化分割结果。在EVIMO2v2数据集上的实验表明，该方法无需完整光流计算即可实现精确的运动分割与平移运动估计，在物体边界处表现优异，具有显著的实时性与可扩展性，适用于机器人与导航等应用场景。
原文摘要 · Abstract (English)
This paper introduces a robust framework for motion segmentation and egomotion estimation using event-based normal flow, tailored specifically for neuromorphic vision sensors. In contrast to traditional methods that rely heavily on optical flow or explicit depth estimation, our approach exploits the sparse, high-temporal-resolution event data and incorporates geometric constraints between normal flow, scene structure, and inertial measurements. The proposed optimization-based pipeline iteratively performs event over-segmentation, isolates independently moving objects via residual analysis, and refines segmentations using hierarchical clustering informed by motion similarity and temporal consistency. Experimental results on the EVIMO2v2 dataset validate that our method achieves accurate segmentation and translational motion estimation without requiring full optical flow computation. This approach demonstrates significant advantages at object boundaries and offers considerable potential for scalable, real-time robotic and navigation applications.
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