Abstract
Object pose tracking is one of the pivotal technologies in multimedia, attracting ever-growing attention in recent years. Existing methods employing traditional cameras encounter numerous challenges such as motion blur, sensor noise, partial occlusion, and changing lighting conditions. The emerging bio-inspired sensors, particularly event cameras, possess advantages such as high dynamic range and low latency, which hold the potential to address the aforementioned challenges. In this work, we present an optical flow-guided 6DoF object pose tracking method with an event camera. A 2D-3D hybrid feature extraction strategy is firstly utilized to detect corners and edges from events and object models, which characterizes object motion precisely. Then, we search for the optical flow of corners by maximizing the event-associated probability within a spatio-temporal window, and establish the correlation between corners and edges guided by optical flow. Furthermore, by minimizing the distances between corners and edges, the 6DoF object pose is iteratively optimized to achieve continuous pose tracking. Experimental results of both simulated and real events demonstrate that our methods outperform event-based state-of-the-art methods in terms of both accuracy and robustness.
Abstract (translated)
物体姿态跟踪是多媒体领域中的关键技术之一，近年来备受关注。现有的使用传统摄像头的方法面临诸如运动模糊、传感器噪声、部分遮挡和光照变化等众多挑战。新兴的仿生传感设备，特别是事件相机（event camera），具有高动态范围和低延迟的优点，有望解决上述问题。在本工作中，我们提出了一种基于事件相机的光流引导6自由度物体姿态跟踪方法。首先采用2D-3D混合特征提取策略，从事件和物体模型中检测出角点和边缘，精确表征了物体的运动。接着，在时空窗口内通过最大化与事件关联的概率来搜索角点的光流，并基于光流建立了角点和边缘之间的关联性。此外，通过最小化角点和边缘之间的距离，迭代优化6自由度物体姿态，实现连续的姿态跟踪。模拟数据和真实事件实验结果表明，我们的方法在精度和鲁棒性方面都优于现有的最先进的事件驱动方法。
URL
https://arxiv.org/abs/2512.21053
