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TransGI: Real-Time Dynamic Global Illumination With Object-Centric Neural Transfer Model.

Yijie Deng, Lei Han, Lu Fang

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    This summary is machine-generated.

    This study introduces TransGI, a neural rendering method for real-time global illumination. It enables high-fidelity rendering with glossy materials and dynamic lighting, overcoming latency limitations in computer graphics.

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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence

    Background:

    • Real-time rendering faces latency challenges, limiting neural graphics algorithms under dynamic lighting.
    • Existing methods either lack material expressiveness or sacrifice quality for speed.

    Purpose of the Study:

    • To develop a novel neural rendering method for real-time, high-fidelity global illumination.
    • To address limitations in material representation and lighting efficiency for dynamic scenes.

    Main Methods:

    • Proposed TransGI, featuring an object-centric neural transfer model for compact and expressive material representation.
    • Introduced a radiance-sharing lighting system with local light probes for efficient illumination under varying light conditions.
    • Implemented using compute shaders and CUDA-based neural networks in a real-time rendering engine.

    Main Results:

    • Achieved real-time performance with frame rendering times under 10 ms.
    • Demonstrated significantly improved rendering quality compared to baseline methods.
    • Supported glossy material effects with low memory overhead.

    Conclusions:

    • TransGI offers a viable solution for real-time, high-fidelity global illumination.
    • The proposed method effectively balances rendering quality, efficiency, and material expressiveness.
    • Enables advanced rendering capabilities in latency-constrained applications.