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TransGI: Real-Time Dynamic Global Illumination With Object-Centric Neural Transfer Model.
IEEE Transactions on Visualization and Computer Graphics
|August 22, 2025
Summary
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.
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.
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