Camera Lens Simulation

CameraLensSimulation implements geometric ray tracing in JAX and provides loss builders and optimizers for lens design. The project includes reference glass catalogs, Zemax-style parsers, plotting utilities, and the LDG-EA evolutionary optimizer for global search.

Features

• JAX-based ray tracing with 64-bit support for rotationally symmetric spherical/planar surfaces (lensgopt/optics/optics.py, lensgopt/optics/shapes.py).
• Lens system data model with paraxial solvers for entrance pupil, sensor plane, and effective focal length (lensgopt/optics/model.py).
• Loss factories to optimize curvatures, separations, and glass choices; ready to pack/unpack subsets of parameters (lensgopt/optics/loss.py).
• Sellmeier glass catalogs with Schott data and helper utilities to resolve refractive indices at selected lines (for example C/D/F) (lensgopt/materials/refractive_index_catalogs.py, lensgopt/materials/databases/schott-optical-glass-overview-excel-format-en.xlsx).
• Visualization of lens layouts and ray fans for debugging or reporting (lensgopt/visualization/lens_vis.py).
• Parsers and ready-made examples, including a Double-Gauss design expressed in a Zemax-compatible text file (lensgopt/parsers/zmax_fmt.py, lensgopt/parsers/lenses/double_gauss_schott.txt, examples/double-gauss-de-example.ipynb).
• Source code for the lens-descriptor-guided evolutionary algorithm (LDG-EA) for global quality-diversity optimization of constrained optical designs. The methodology is summarized in the diagram below.

Getting started

Prerequisites:

• A C++17 toolchain if you want to build HillVallEA yourself (e.g., MSVC Build Tools on Windows).
• Python 3.10+ (required by the pinned dependency set, including JAX).
• For accurate optics calculations, enable JAX 64-bit mode.

Install:

pip install -e .  # installs dependencies, auto-initializes submodules, and builds hillvallimpl

Recompiling Only the C++ Extension

If you change only the C++ code and want to rebuild without reinstalling the package:

python setup.py build_ext --inplace

---

Fetching External Dependencies

Initialize external dependencies with git submodules:

git submodule update --init --recursive

This step is optional if you install with pip install -e ., because build hooks automatically initialize external/HillVallEA when needed.

Or clone the external repository explicitly:

cd external
git clone https://github.com/AntKirill/HillVallEA

Repository map

• lensgopt/optics/: ray tracing kernels, paraxial solvers, and loss factories.
• lensgopt/materials/: glass catalogs and Sellmeier helpers.
• lensgopt/parsers/: Zemax-format parser plus canned lens builders (e.g., Double-Gauss).
• lensgopt/visualization/: plotting utilities for lens layouts, rays, and landscapes.
• examples/: notebooks and scripts showing optimization workflows.

Performance (Double-Gauss benchmark)

From the final cell of examples/double-gauss-example.ipynb on a Windows / Python 3.10 setup (Intel Core Ultra 9 185H, 16 threads, 32 GB RAM):

Kernel	Time (ms)	Speed vs MATLAB loss
JAX Loss	1.5	8.78× faster
JAX Grad	9.0	1.50× faster
MATLAB Loss	13.5	1.0× (baseline)
PyTorch Loss	72.3	0.19×

JAX wins both absolute loss computation time and gradient computation speed, while still using double precision for accuracy.

Quick example (Double-Gauss)

import jax
from examples.double_gauss_objective import DoubleGaussObjective

jax.config.update("jax_enable_x64", True)

objective = DoubleGaussObjective()
x0_cont, x0_ids = objective.init_from_templates()
theta0 = objective.pack_theta(x0_cont, x0_ids)

# Plot the Double-Gauss design
fig, ax, loss0 = objective.visualize(
    theta=theta0,
    use_latex=False,
    num_rays=10,
)
print(f"Loss = {loss0:.8f}")
fig.savefig("double_gauss.png", dpi=300, bbox_inches="tight")

To optimize a subset of parameters (curvatures, gaps, glass IDs), pass objective.objective_theta to an optimizer. The notebook in examples/double-gauss-de-example.ipynb shows a Differential Evolution workflow with this objective.

Citation

If you use Camera Lens Simulation, please cite:

• Paper: https://arxiv.org/abs/2601.22075

@article{antonov2026ldgea,
  title={Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice},
  author={Antonov, Kirill and Tukker, Teus and Botari, Tiago and B\"ack, Thomas H. W. and Kononova, Anna V. and van Stein, Niki},
  journal={arXiv preprint arXiv:2601.22075},
  year={2026},
  doi={10.48550/arXiv.2601.22075},
  url={https://arxiv.org/abs/2601.22075}
}