Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120% while also achieving the lowest latency. This performance gap is further increased to 130% by adapting our tracker to real data with a novel self-supervision strategy.

翻译：由于具有高时间分辨率、对运动模糊的增强鲁棒性以及非常稀疏的输出，事件相机已被证明即使在具有挑战性的场景中，也是低延迟和低带宽特征跟踪的理想选择。现有的事件相机特征跟踪方法要么是手工设计的，要么是从第一原理推导而来的，但需要大量的参数调整，对噪声敏感，并且由于未建模的效应而无法推广到不同场景。为了解决这些缺陷，我们引入了第一个用于事件相机的数据驱动特征跟踪器，该跟踪器利用低延迟事件来跟踪灰度帧中检测到的特征。我们通过一种新颖的帧注意力模块实现了鲁棒的性能，该模块在特征轨迹之间共享信息。通过从合成数据到真实数据的零样本直接迁移，我们的数据驱动跟踪器在相对特征寿命上比现有方法高出高达120%，同时实现了最低的延迟。通过采用一种新颖的自监督策略将我们的跟踪器适应真实数据，这一性能差距进一步扩大至130%。