Eye tracking is crucial for human-computer interaction in different domains.
Conventional cameras encounter challenges such as power consumption and image
quality during different eye movements, prompting the need for advanced
solutions with ultra-fast, low-power, and accurate eye trackers. Event cameras,
fundamentally designed to capture information about moving objects, exhibit low
power consumption and high temporal resolution. This positions them as an
alternative to traditional cameras in the realm of eye tracking. Nevertheless,
existing event-based eye tracking networks neglect the pivotal sparse and
fine-grained temporal information in events, resulting in unsatisfactory
performance. Moreover, the energy-efficient features are further compromised by
the use of excessively complex models, hindering efficient deployment on edge
devices. In this paper, we utilize Point Cloud as the event representation to
harness the high temporal resolution and sparse characteristics of events in
eye tracking tasks. We rethink the point-based architecture PEPNet with
preprocessing the long-term relationships between samples, leading to the
innovative design of FAPNet. A frequency adaptive mechanism is designed to
realize adaptive tracking according to the speed of the pupil movement and the
Inter Sample LSTM module is introduced to utilize the temporal correlation
between samples. In the Event-based Eye Tracking Challenge, we utilize vanilla
PEPNet, which is the former work to achieve the $p_{10}$ accuracy of 97.95\%.
On the SEET synthetic dataset, FAPNet can achieve state-of-the-art while
consuming merely 10\% of the PEPNet's computational resources. Notably, the
computational demand of FAPNet is independent of the sensor's spatial
resolution, enhancing its applicability on resource-limited edge devices.

利用点云作为事件表示来利用眼动任务中事件的高时间分辨率和稀疏特性，重新思考基于点的架构 PEPNet 并引入长期样本关系预处理的创新设计 FAPNet，以实现自适应跟踪和利用样本之间的时间相关性的频适应机制。在 SEET 合成数据集上，FAPNet 可以以仅消耗 PEPNet 计算资源的 10％的状态实现，并且其计算需求不依赖于传感器的空间分辨率，提高其在资源有限的边缘设备上的适用性。