Abstract
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.
Abstract (translated)
本文介绍了一个基于事件的法线流进行运动分割和自运动估计的稳健框架，专门针对神经形态视觉传感器。与依赖于光流或显式深度估计的传统方法不同，我们的方法利用了稀疏、高时间分辨率的事件数据，并结合了法线流、场景结构和惯性测量之间的几何约束条件。所提出的优化管道迭代地执行事件过度分割，通过残差分析隔离独立移动的对象，并使用基于运动相似性和时间一致性信息的分层聚类来细化分割结果。在EVIMO2v2数据集上的实验结果验证了我们的方法能够实现准确的分割和无须完整光流计算的平移运动估计。此方法在对象边界处表现出了显著优势，为可扩展、实时的机器人和导航应用提供了巨大的潜力。
URL
https://arxiv.org/abs/2507.14500
