用事件相机实现无需训练的新物体6D姿态追踪
Event6D: Event-based Novel Object 6D Pose Tracking
- 通过稀疏事件流重建任意时间点的图像与深度图
- 在真实场景中无需微调，120帧以上保持高精度追踪
- 适合快速动态场景下的机器人抓取与导航任务
事件相机具备微秒级延迟，适用于高速动态场景中的6D物体姿态追踪，克服了传统RGB和深度流水线因运动模糊和像素大位移导致的问题。我们提出EventTrack6D框架，通过在最近一次深度测量基础上对事件流进行双重建（光度与几何），从稀疏事件中恢复密集的视觉与几何线索，实现对未见过物体的泛化追踪。该方法运行速度超过120 FPS，可在快速运动下保持时序一致性。为支持训练与评估，我们构建了一个大规模合成数据集及两个互补的评测集（包含真实与仿真事件数据）。仅在合成数据上训练的模型可直接应用于真实场景而无需微调，在多种物体与运动模式下均表现准确。代码与数据集已公开。
原文摘要 · Abstract (English)
Event cameras provide microsecond latency, making them suitable for 6D object pose tracking in fast, dynamic scenes where conventional RGB and depth pipelines suffer from motion blur and large pixel displacements. We introduce EventTrack6D, an event-depth tracking framework that generalizes to novel objects without object-specific training by reconstructing both intensity and depth at arbitrary timestamps between depth frames. Conditioned on the most recent depth measurement, our dual reconstruction recovers dense photometric and geometric cues from sparse event streams. Our EventTrack6D operates at over 120 FPS and maintains temporal consistency under rapid motion. To support training and evaluation, we introduce a comprehensive benchmark suite: a large-scale synthetic dataset for training and two complementary evaluation sets, including real and simulated event datasets. Trained exclusively on synthetic data, EventTrack6D generalizes effectively to real-world scenarios without fine-tuning, maintaining accurate tracking across diverse objects and motion patterns. Our method and datasets validate the effectiveness of event cameras for event-based 6D pose tracking of novel objects. Code and datasets are publicly available at https://chohoonhee.github.io/Event6D.
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