Citation and History

If you use DeepLens in your research, please cite the relevant papers.

Primary Citation

Curriculum Learning for Ab Initio Deep Learned Refractive Optics

@article{yang2024curriculum,
  title={Curriculum learning for ab initio deep learned refractive optics},
  author={Yang, Xinge and Fu, Qiang and Heidrich, Wolfgang},
  journal={Nature Communications},
  volume={15},
  number={1},
  pages={6572},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

Paper link: https://www.nature.com/articles/s41467-024-50835-7

History of DeepLens

Evolution Timeline

2021: Early Development

  • Initial concept and prototyping

  • Basic ray tracing framework

  • Differentiable optimization proof-of-concept

2022: Core Features

  • Comprehensive optical surface library

  • Wave optics integration

  • Sensor and ISP simulation

  • First automated lens designs

2023: Advanced Capabilities

  • Aberration-aware imaging (TPAMI 2023)

  • Neural surrogate models (PSFNet)

  • End-to-end optimization framework

  • Multi-wavelength support

2024: Public Release

  • Nature Communications publication

  • Open-source release

  • Hybrid refractive-diffractive support (SIGGRAPH Asia 2024)

  • Community building

2025: Ongoing Development

  • Enhanced GPU acceleration

  • Distributed computing support

  • Polarization ray tracing

  • Non-sequential systems

Key Milestones

Date

Milestone

2021

Project initiated at KAUST

2022

First fully automated lens design from scratch

2023 Jun

TPAMI paper on aberration-aware depth estimation

2024 Jul

Nature Communications paper published

2024 Aug

Open-source release on GitHub

2024 Dec

SIGGRAPH Asia paper on hybrid lenses

2025

Community edition with extended features

Contributors

Core Team

  • Xinge Yang - Project Lead, Main Developer

  • Qiang Fu - Optical Design Consultant

  • Wolfgang Heidrich - Principal Investigator

Major Contributors

  • Congli Wang - Hybrid lens module

  • Ni Chen - Wave optics algorithms

See full list at: https://github.com/singer-yang/DeepLens/graphs/contributors

Acknowledgments

Funding

This project was supported by:

  • King Abdullah University of Science and Technology (KAUST)

  • Visual Computing Center (VCC)

  • Computational Imaging Lab

Collaborations

We thank our collaborators:

  • MIT Media Lab

  • Stanford Computational Imaging Lab

  • Various industry partners

Open Source Community

Special thanks to our open-source community for:

  • Bug reports and fixes

  • Feature suggestions

  • Documentation improvements

  • Testing and validation

Awards and Recognition

  • Nature Communications - Featured article (2024)

  • SIGGRAPH Asia - Technical paper (2024)

  • TPAMI - Journal publication (2023)

Media Coverage

DeepLens has been featured in:

  • Nature Communications press release

  • KAUST Discovery magazine

  • Computer graphics and optics news outlets

Research Using DeepLens

If you have published research using DeepLens, let us know! We’d love to feature it here.

Example applications:

  • Automated lens design for imaging systems

  • Computational photography research

  • Virtual reality optics

  • Smartphone camera optimization

  • Microscopy and scientific imaging

  • Automotive camera systems

Contact

For academic collaborations:

For commercial licensing:

Community

Join our community:

License

DeepLens is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).

What this means:

  • ✓ Use for academic and research purposes

  • ✓ Modify and build upon the work

  • ✓ Share with attribution

  • ✗ Commercial use without permission

For commercial licensing, please contact the authors.

Future Directions

Planned Features

  • Extended material database

  • Tolerance analysis tools

  • Manufacturing interface

  • Cloud-based optimization

  • Real-time preview

  • VR/AR integration

Research Directions

  • Quantum optical systems

  • Freeform optics

  • Holographic displays

  • Neuromorphic imaging

  • Computational microscopy

Get Involved

Ways to contribute:

  • Use DeepLens in your research

  • Report bugs and issues

  • Suggest new features

  • Contribute code

  • Write documentation

  • Share your designs

See Contributing to DeepLens for details.

See Also