用超薄无镜镜头实现近眼实时眼球追踪，让头显更轻薄
FlatTrack: Eye-tracking with ultra-thin lensless cameras
- 用无镜镜头+轻量神经网络，可贴在眼镜框上近眼采集
- 实测性能媲美传统镜头追踪器，帧率超125fps
- 适合追求轻量化的眼动追踪场景，如AR/VR设备
现有眼动追踪器依赖厚复合光学镜头，需与眼睛保持一定距离，导致可穿戴设备整体臃肿，尤其影响增强现实和虚拟现实头戴设备。本文通过构建基于掩码的无镜镜头相机，配合定制的轻量级深度神经网络算法，使摄像头可紧贴眼球放置于眼镜框架内，实现超薄轻便的眼动追踪系统。我们收集了大量近眼无镜镜头数据及对应的校准注视方向数据，用于训练注视追踪网络。通过真实与仿真实验验证，所提系统性能达到传统镜头追踪器水平，同时显著降低厚度与体积。此外，其注视回归器支持实时运行（>125 fps）。
原文摘要 · Abstract (English)
Existing eye trackers use cameras based on thick compound optical elements, necessitating the cameras to be placed at focusing distance from the eyes. This results in the overall bulk of wearable eye trackers, especially for augmented and virtual reality (AR/VR) headsets. We overcome this limitation by building a compact flat eye gaze tracker using mask-based lensless cameras. These cameras, in combination with co-designed lightweight deep neural network algorithm, can be placed in extreme close proximity to the eye, within the eyeglasses frame, resulting in ultra-flat and lightweight eye gaze tracker system. We collect a large dataset of near-eye lensless camera measurements along with their calibrated gaze directions for training the gaze tracking network. Through real and simulation experiments, we show that the proposed gaze tracking system performs on par with conventional lens-based trackers while maintaining a significantly flatter and more compact form-factor. Moreover, our gaze regressor boasts real-time (>125 fps) performance for gaze tracking.
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