用神经网络替代传统光路计算，实现高效精准的光学系统建模
Efficient Proxy Raytracer for Optical Systems using Implicit Neural Representations
- 用隐式神经表示直接学习入射光束到出射光束的映射关系
- 在9个光学系统上实现位置误差约1μm、角度偏差0.01度
- 适合需要快速光学仿真但不需逐表面计算的工程场景
光线追踪是建模光学系统的重要技术，传统方法需逐面计算，计算成本高。本文提出Ray2Ray，利用隐式神经表示，在单次端到端前向传播中实现光学系统的高效建模，无需逐表面计算。该方法学习从光源发出的光线到通过光学系统后的输出光线之间的物理精确映射。我们在9个现成光学系统上训练了Ray2Ray，使估计输出光线的位置误差达到约1μm，角度偏差约为0.01度。研究展示了神经表示作为光学光线追踪代理的巨大潜力。
原文摘要 · Abstract (English)
Ray tracing is a widely used technique for modeling optical systems, involving sequential surface-by-surface computations, which can be computationally intensive. We propose Ray2Ray, a novel method that leverages implicit neural representations to model optical systems with greater efficiency, eliminating the need for surface-by-surface computations in a single pass end-to-end model. Ray2Ray learns the mapping between rays emitted from a given source and their corresponding rays after passing through a given optical system in a physically accurate manner. We train Ray2Ray on nine off-the-shelf optical systems, achieving positional errors on the order of 1μm and angular deviations on the order 0.01 degrees in the estimated output rays. Our work highlights the potential of neural representations as a proxy for optical raytracer.
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