用光学编码直接提取注视特征，实现3.4毫秒超低延迟追踪。
Low Latency Gaze Tracking via Latent Optical Sensing
- 通过微透镜阵列与掩膜实现空间复用光学编码，直接生成关键特征。
- 端到端延迟仅3.4毫秒，比现有系统更快，功耗更低。
- 适合需要实时交互的场景，如脑机接口、虚拟现实等。
我们提出一种实时注视追踪系统，通过全被动光学编码器直接获取任务相关的潜在特征。该方法不生成和处理全分辨率图像，而是利用微透镜阵列与定制二进制铬掩膜进行空间复用光学编码，生成一组紧凑测量值，足以支持注视方向估计。通过在光学域内集成感知与特征提取，系统避免了高带宽图像读出，显著降低计算开销。编码测量值由4×4光晶体管阵列捕获，并通过轻量级神经网络映射为注视方向。原型系统实现3.4毫秒的端到端传感-推理延迟，优于已有研究系统。我们在仿真和真实数据上验证了方法的有效性，在保持竞争性追踪精度的同时，大幅提升了延迟与能效。本工作展示了面向任务的光学感知在超低延迟、高效人机交互中的潜力。
原文摘要 · Abstract (English)
We present a real-time gaze tracking system that directly acquires task-relevant latent features using a fully passive optical encoder. Instead of forming and processing full-resolution images, our approach leverages a microlens array with a co-designed binary chromium mask to perform spatially multiplexed optical encoding, producing a compact set of measurements sufficient for gaze estimation. By integrating sensing and feature extraction in the optical domain, the proposed system eliminates the need for high-bandwidth image readout and substantially reduces computational overhead. The encoded measurements are captured by a 4 x 4 phototransistor array and mapped to gaze direction using a lightweight neural network. Our proof-of-concept prototype enables an end-to-end sensing-to-inference latency of 3.4 ms, outperforming published research systems. We demonstrate the effectiveness of our approach on both simulated and real-world data, achieving competitive gaze estimation accuracy while significantly improving latency and energy efficiency compared to conventional camera-based pipelines. This work highlights the potential of task-driven optical sensing for ultra-low-latency, computationally efficient human-computer interaction systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。