Xinge Yang (杨辛格)

I am a Ph.D. student at KAUST Computational Imaging Group, working with Prof. Wolfgang Heidrich. My research interests include deep learning for optical design, computational cameras, and optics-aware computational photography.

I maintain an awesome differentiable lens simulator DeepLens. It can do end-to-end differentiable simulation for optics, camera sensor, and image processing. Feel free to drop me an email if you are interested!

Email  /  Google Scholar  /  Github  /  LinkedIn  /  知乎

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News

  • 11/2024: I will attend Siggraph Asia this year, see you in Tokyo.
  • 08/2024: "Curriculum Learning for ab initio Deep Learned Refractive Optics" is acceptted by Nature Communications !
  • 03/2024: DeepLens is open-sourced, build your End-to-End lens design pipeline with 5 lines of Python code!
  • 10/2023: I started my internship at Meta, working on gradient-based optical design for AR waveguide.
  • 09/2023: We released an automated lens design demo AutoLens .
  • 07/2023: Our paper, "Aberration-Aware Depth-from-Focus" is accepted by ICCP and TPAMI .

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    Education

  • 2022 - present: Ph.D. in Computer Science, KAUST, Saudi Arabia.
  • 2020 - 2022: M.Sc. in Computer Science, KAUST, Saudi Arabia.
  • 2016 - 2020: B.Sc. in Physics (major) and Computer Science (minor), USTC, China.

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    Working

  • 07/2024 - 11/2024: Research scientist Intern, XR Tech Camera & Sensing, Meta Reality Lab, Sunnyvale, CA, USA.
  • 10/2023 - 01/2024: Research scientist Intern, Optics & Display Research, Meta Reality Lab Research, Redmond, WA, USA

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    Research

    My research focuses on two topics:

  • Deep learning for optical design
  • Computational camera and photography

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    First author papers:
    		End-to-End Hybrid Refractive-Diffractive Lens Design with Differentiable Ray-Wave Model
    Xinge Yang, Matheus Souza, Kunyi Wang, Praneeth Chakravarthula, Qiang Fu, Wolfgang Heidrich
    Siggraph Asia 2024. Paper (Arxiv) / Paper (PDF) / Supp (PDF)

  • Differentiable ray-tracing and wave-propagation model.
  • End-to-End hybrid refractive-diffractive lenses design with prototypes.

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    		Image Quality Is Not All You Want: Task-Driven Lens Design for Image Classification
    Xinge Yang, Qiang Fu, Yunfeng Nie, Wolfgang Heidrich
    Arxiv prepint. Paper (Arxiv) / Paper (PDF) / Supp (PDF)

  • A new End-to-End optical design methedology: a well-trained network as objective.
  • TaskLens: better computer vision performance with fewer lens elements.

    		• Aberration-Aware Depth-from-Focus
    Xinge Yang, Qiang Fu, Mohamed Elhoseiny, Wolfgang Heidrich
    TPAMI & ICCP 2023. Paper (IEEE) / Paper (PDF) / Supp (PDF) / Project page / Code

  • An implicite network for real-lens spatially-varying focus-dependent (4D) PSFs representation.
  • Generalize depth-from-focus network from synthetic data to real data by considering optical aberrations.

    		• Curriculum Learning for ab initio Deep Learned Refractive Optics
    Xinge Yang, Qiang Fu, Wolfgang Heidrich
    Nature Communications 2024. Paper (Nature) / Paper (PDF) / Supp (PDF) / Code / Video

  • Automated lens design from scratch with differentiable ray tracing.
  • DeepLens framework for (1) differentiable ray tracing simulation, (2) end-to-end lens-network co-design.

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    Co-author papers:
    		Tolerance-Aware Deep Optics
    Jun Dai, Liqun Chen, Xinge Yang, Yuyao Hu, Jinwei Gu, Tianfan Xue
    Arxiv prepint 2025. Paper (Arxiv) Project page

  • Considering lens manufacturing tolerances in end-to-end lens design.

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    		End-to-end Optimization of Fluidic Lenses
    Mulun Na, Hector Jimenez-Romero, Xinge Yang, Jonathan Klein, Dominik L. Michels, Wolfgang Heidrich
    Siggraph Asia 2024. Paper (PDF), Project page

  • Differentiable edge shape defined lens surface optimization.
  • Fast prototyping with fluidic manufacturing.

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    Miscs

  • More about me: I like cats, photography, new techs, and most sports (especially basketball and water sports). I lived in China, Singapore, Saudi Arabia, and the US, and want to live in different places to experience the lifestyle of local people. I enjoy adventures and challenges. I love my life.

  • Peer review statement: I am pleased to be invited for peer review in my research field. I will try my best to complete the review within two weeks. I apply the same high standards to every paper, aiming for the utmost potential of the topic. However, I do not expect the authors to address every suggestion I make and leave the final judgment to the associate editor.

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    The website template is from Dr. John Barron.