Reproduce “Keypoint-based Dynamic Object 6-DoF Pose Tracking via Event Camera” on OpenResearch
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About this paper
Zhe Wang, Qijin Song, Zihao Li, Jingyu Xiao, Weibang Bai
A keypoint-based pipeline for 6-DoF pose tracking of dynamic objects using event cameras is presented, featuring a lightweight detector and a density-guided tracking method. This approach demonstrates enhanced accuracy and robustness for objects with complex geometries under high-speed motion compared to state-of-the-art event-based techniques.
Key insights
- Leveraging event density and polarity-aware matching significantly enhances keypoint tracking stability for dynamic objects, even with sparse event data.
- A lightweight neural network combined with a local refinement module can effectively extract precise keypoints from event streams, making it adaptable to diverse object geometries including curved surfaces.
- Integrating an Extended Kalman Filter provides temporal robustness, mitigating drift and jitter in keypoint tracking and maintaining accuracy during high-speed motion.
Summary by alphaXiv.
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