Efficient Proxy Raytracer for Optical Systems using Implicit Neural Representations
This work addresses efficiency problems for optical system designers by providing a faster alternative to surface-by-surface computations, though it is incremental as it applies neural methods to an existing domain.
The authors tackled the computational intensity of traditional ray tracing in optical systems by proposing Ray2Ray, a method using implicit neural representations to model optical systems efficiently, achieving positional errors of about 1μm and angular deviations of 0.01 degrees in output rays.
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.