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2. Real-time 3D Visualization of Radiance Fields on Light Field Displays

 # Real-time 3D Visualization of Radiance Fields on Light Field Displays

  ![](/sites/default/files/styles/wide/public/publications/teaser_final.png?itok=oSjvokd_)

 Radiance fields, including their recent efficient forms such as 3D Gaussian Splatting and Sparse Voxels, have revolutionized photorealistic 3D scene visualization by enabling high-fidelity reconstruction of complex environments, making them a natural match for light field displays. However, integrating these technologies presents significant computational challenges, as light field displays require many high-resolution renderings from slightly shifted viewpoints, while radiance fields rely on computationally intensive volume rendering, which is intractable to achieve real-time speeds even with efficient scene representations. In this paper, we propose a unified and efficient framework for real-time radiance field rendering on light field displays. Rather than re-rendering each view independently, our method converts the input radiance field into shared intermediate sweeping planes that can be efficiently composited into dense light-field views in a single pass. Our method prioritizes shared, non-directional plane caching for real-time performance, trading fine view-dependent color effects for a modest increase in intermediate memory usage. Our framework generalizes across different scene representations without retraining and avoids repeated computation across views. We further demonstrate a real-time interactive application on a Looking Glass display, achieving 200+ FPS at 512p across 45 rendered views and enabling seamless, immersive 3D interactive viewing experiences. On standard benchmarks, our method achieves up to 22× speedup compared to independently rendering each view, while largely preserving image quality.

 ## Authors

[Jonghyun Kim](/person/jonghyun-kim)

[Cheng Sun](/person/cheng-sun)

[Michael Stengel](/person/michael-stengel)

Matthew Chan (NVIDIA)

Andrew Russell (NVIDIA)

[Jae-Hyun Jung](/person/jae-hyun-jung)

Wil Braithwaite (NVIDIA)

[Shalini De Mello](/person/shalini-de-mello)

[David Luebke](/person/david-luebke)

 ## Publication Date

Friday, July 17, 2026

 ## Published in

[HPG 2026](https://dl.acm.org/doi/10.1145/3820023)

 ## Research Area

[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Computer Graphics](/research-area/computer-graphics)

[Computer Vision](/research-area/computer-vision)

[VR, AR and Display Technology](/research-area/virtual-augmented-reality)

 ## External Links

[Project Page](https://research.nvidia.com/labs/amri/projects/g2lf/)

 ## Uploaded Files

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