US 2026/0087600 A1 · PER-ASSET DENOISING FOR REAL-TIME RENDERING OF NEURAL RADIANCE FIELDS (NERFS)

Per-asset denoising for real-time rendering of neural radiance fields (nerfs)

AssigneesAdobe Inc., The Regents of the University of California

InventorsSai Bi, Zexiang Xu, Xin Sun, Miloš Hašan, Kunal Gupta, Kevin Blackburn-Matzen, Kalyan Krishna Sunkavalli, Kai Zhang, Julien Olivier Victor Philip, Fujun Luan, Manmohan Chandraker, Iliyan Atanasov Georgiev

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Abstract

In implementing per-asset denoising for real-time rendering of neural radiance fields (NeRFs), a processing device receives a three-dimensional (3D) representation of a scene as a NeRF. The processing device generates an intermediate rendering of the scene using the NeRF. The intermediate rendering is denoised using a machine-learning model to generate a final rendering. The machine-learning model is trained on another rendering of this scene, which was rendered using a non-real-time, high-quality rendering scheme. In other words, the machine-learning model is optimized for each scene and provides a lightweight denoising network to provide real-time NeRF rendering while maintaining the high-quality visuals of non-real-time rendering schemes. The final rendering is then presented via a display device.

Claims20

Dependent claims

A method comprising:

receiving, by a processing device, a three-dimensional (3D) representation of a scene as a neural radiance field (NeRF);
generating, by the processing device and using the NeRF, a first rendering of the scene;
generating, using a machine-learning model, a second rendering of the scene by denoising the first rendering, the machine-learning model trained on a third rendering of the scene;
and
presenting, by the processing device, the second rendering on a display device.

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