Point-spread-function (PSF) engineering is a well-established computational
imaging technique that uses phase masks and other optical elements to embed
extra information (e.g., depth) into the images captured by conventional CMOS
image sensors. To date, however, PSF-engineering has not been applied to
neuromorphic event cameras; a powerful new image sensing technology that
responds to changes in the log-intensity of light.
This paper establishes theoretical limits (Cram\'er Rao bounds) on 3D point
localization and tracking with PSF-engineered event cameras. Using these
bounds, we first demonstrate that existing Fisher phase masks are already
near-optimal for localizing static flashing point sources (e.g., blinking
fluorescent molecules). We then demonstrate that existing designs are
sub-optimal for tracking moving point sources and proceed to use our theory to
design optimal phase masks and binary amplitude masks for this task. To
overcome the non-convexity of the design problem, we leverage novel implicit
neural representation based parameterizations of the phase and amplitude masks.
We demonstrate the efficacy of our designs through extensive simulations. We
also validate our method with a simple prototype.

点扩散函数（PSF）工程是一种井然有序的计算成像技术，它利用相位掩模和其他光学元件将额外的信息（如深度）嵌入常规 CMOS 图像传感器捕获的图像中。本文通过理论界限（Cramér Rao 界限）建立了使用 PSF 工程事件相机进行三维点定位和跟踪的方法。利用这些界限，首先证明了现有的 Fisher 相位掩模在定位静态闪烁点源（如闪烁荧光分子）时已经接近最优。然后证明了现有设计在跟踪移动点源方面是次优的，并利用我们的理论为此任务设计了最优相位掩模和二值振幅掩模。为了克服设计问题的非凸性，我们利用基于新颖的隐式神经表示的参数化方法对相位和振幅掩模进行了描述。通过大量的仿真实验证明了我们设计的有效性。我们还用一个简单的原型进行了验证。