Computational Caustic Design for Surface Light Source

TL;DR

This paper introduces a differentiable rendering framework to optimize surface light sources and design freeform caustic lenses that accurately reproduce real-world illumination patterns, surpassing traditional point-source models.

Contribution

It presents a novel method to model real surface light sources with optimized point sources and integrates this into a lens design process using a physically-based, differentiable rendering approach.

Findings

More accurate representation of real surface light sources.

Caustic lenses that closely match target light distributions.

Effective optimization of light source parameters and lens shape.

Abstract

Designing freeform surfaces to control light based on real-world illumination patterns is challenging, as existing caustic lens designs often assume oversimplified point or parallel light sources. We propose representing surface light sources using an optimized set of point sources, whose parameters are fitted to the real light source's illumination using a novel differentiable rendering framework. Our physically-based rendering approach simulates light transmission using flux, without requiring prior knowledge of the light source's intensity distribution. To efficiently explore the light source parameter space during optimization, we apply a contraction mapping that converts the constrained problem into an unconstrained one. Using the optimized light source model, we then design the freeform lens shape considering flux consistency and normal integrability. Simulations and physical experiments show our method more accurately represents real surface light sources compared to point-source approximations, yielding caustic lenses that produce images closely matching the target light distributions.