Xinge Yang (杨辛格)

I am a Ph.D. student at VCC Computational Imaging Group at KAUST, working with Prof. Wolfgang Heidrich. My research interests include differentiable optical simulation, computational imaging/photography, and ray-tracing based rendering.

I like cats, cameras, new techs, and most of sports (especially basketball, hiking and marathon). I have lived in China, Singapore, Saudi Arabia, and the US.

I am maintaining an awesome differentiable ray tracer DeepLens. It can simulate raw captures of a camera lens and optimize it. Feel free to drop me an email if you are interested!

Email  /  CV  /  Github  /  LinkedIn  /  知乎

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News

  • 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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    Experience

  • 05/2022 - present: Ph.D. student in Computer Science, KAUST, Thuwal, Saudi Arabia.
  • 10/2023 - 01/2024: Research Scientist Intern, Optics and Display Research, Meta Reality Lab, Redmond, WA.
  • 08/2020 - 05/2022: M.Sc. in Computer Science, KAUST, Thuwal, Saudi Arabia.
  • 09/2016 - 06/2020: B.Sc. in Physics (major) and Computer Science (minor), USTC, Hefei, China.

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    Research

    I do differentiable optical simulation, and apply it to End-to-End lens design and computational photography.

    		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, supp

  • A new End-to-End optical design methedology: use a well-trained network as objective.
  • Lens design can start from scratch and gets a larger design space. Our lenses have higher image classification accuracy with fewer elements.

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    		Aberration-Aware Depth-from-Focus
    Xinge Yang, Qiang Fu, Mohamed Elhoseiny, Wolfgang Heidrich
    ICCP & TPAMI 2023. paper, supp, project, code

  • Train a network to represent spatially-varying focus-dependent (4D) PSFs for a real camera lens.
  • Improve the generalizability of depth-from-focus network models by considering optical aberrations during the training.

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    		Curriculum Learning for ab initio Deep Learned Refractive Optics (DeepLens)
    Xinge Yang, Qiang Fu, Wolfgang Heidrich
    Arxiv prepint. paper, supp, code, video
    OSA Imaging and Applid Optics Congress - Computational Optical Sensing and Imaging (COSI) 2022 (oral): paper, project, code

  • Differentiable (gradient-based) ray tracing will be a game-changer for optical design.
  • Fully automated design for both classical and computational lenses from scratch.

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    The website template is from Dr. John Barron.