Aperture-Aware Lens Design
Arjun Teh, Ioannis Gkioulekas, Matthew O'Toole
Abstract
Optics designers use simulation tools to assist them in designing lenses for various applications. Commercial tools rely on finite differencing and sampling methods to perform gradient-based optimization of lens design objectives. Recently, differentiable rendering techniques have enabled more efficient gradient calculation of these objectives. However, these techniques are unable to optimize for light throughput, often an important metric for many applications.
We develop a method for calculating the gradients of optical systems with respect to both focus and light throughput. We formulate lens performance as an integral loss over a dynamic domain, which allows for the use of differentiable rendering techniques to calculate the required gradients. We also develop a ray tracer specifically designed for refractive lenses and derive formulas for calculating gradients that simultaneously optimize for focus and light throughput. Explicitly optimizing for light throughput produces lenses that outperform traditional optimized lenses that tend to prioritize for only focus. To evaluate our lens designs, we simulate various applications where our lenses: (1) improve imaging performance in low-light environments, (2) reduce motion blur for high-speed photography, and (3) minimize vignetting for large-format sensors.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers1
Ask how each one uses itBuilds on11
- DR.JIT: a just-in-time compiler for differentiable renderingWenzel Jakob, Sébastien Speierer, Nicolas Roussel, Delio ViciniSIGGRAPH 2022 · 160 citations
- Path-space differentiable renderingCheng Zhang, Bailey Miller, Kai Yan, Ioannis Gkioulekas et al.SIGGRAPH 2020 · 155 citations
- Differentiable signed distance function renderingDelio Vicini, Sébastien Speierer, Wenzel JakobSIGGRAPH 2022 · 112 citations
- Radiative backpropagation: an adjoint method for lightning-fast differentiable renderingMerlin Nimier-David, Sébastien Speierer, Benoît Ruiz, Wenzel JakobSIGGRAPH 2020 · 107 citations
- Path replay backpropagation: differentiating light paths using constant memory and linear timeDelio Vicini, Sébastien Speierer, Wenzel JakobSIGGRAPH 2021 · 97 citations
Related papers
- The Differentiable Lens: Compound Lens Search over Glass Surfaces and Materials for Object DetectionGeoffroi Côté, Fahim Mannan, Simon Thibault, Jean-François Lalonde et al.CVPR 2023
- End-to-end complex lens design with differentiate ray tracingQilin Sun, Congli Wang, Qiang Fu, Xiong Dun et al.SIGGRAPH 2021 · 154 citations
- Adjoint nonlinear ray tracingArjun Teh, Matthew O'Toole, Ioannis GkioulekasSIGGRAPH 2022 · 19 citations
- A Differentiable Wave Optics Model for End-To-End Computational Imaging System OptimizationChi-Jui Ho, Yash Belhe, Steve Rotenberg, Ravi Ramamoorthi et al.ICCV 2025 · 1 citation
- Plateau-Reduced Differentiable Path TracingMichael Fischer, Tobias RitschelCVPR 2023
