18178817. JOINT NEURAL DENOISING OF SURFACES AND VOLUMES simplified abstract (NVIDIA Corporation)

JOINT NEURAL DENOISING OF SURFACES AND VOLUMES

Organization Name

NVIDIA Corporation

Inventor(s)

Nikolai Till Hofmann of Nuremberg (DE)

Jon Niklas Theodor Hasselgren of Bunkeflostrand (SE)

Carl Jacob Munkberg of Malmö (SE)

JOINT NEURAL DENOISING OF SURFACES AND VOLUMES - A simplified explanation of the abstract

This abstract first appeared for US patent application 18178817 titled 'JOINT NEURAL DENOISING OF SURFACES AND VOLUMES

Simplified Explanation

In this patent application, a method for denoising images rendered using Monte Carlo sampled ray tracing is described. The technique focuses on improving image quality when low sample counts are used, especially in scenes that include volumes in addition to surface geometry. By jointly denoising surfaces and volumes, the method enables real-time denoising of combined volume and surface components from low sample count renderings.

• Explanation of the patent:

 * Decomposition of at least one rendered image into volume and surface layers
 * Utilization of spatio-temporal neural denoisers for both surface and volume components
 * Compositing of denoised surface and volume components using learned weights and denoised transmittance
 * Outperformance of current denoisers in scenes containing both surfaces and volumes, producing temporally stable results at interactive rates

Potential Applications

The technology described in this patent application can be applied in various fields such as computer graphics, virtual reality, augmented reality, and gaming industries.

Problems Solved

• Improved image quality in ray traced scenes with low sample counts
• Real-time denoising of complex scenes containing volumes and surfaces
• Enhanced visual experience for users in interactive applications

Benefits

• Enhanced image quality in real-time rendering
• Reduction of noise in scenes with volumes and surfaces
• Improved performance and visual fidelity in interactive applications

Potential Commercial Applications

• Video game development
• Virtual reality experiences
• Film and animation production
• Architectural visualization

Possible Prior Art

One possible prior art in this field is the use of traditional denoising techniques in computer graphics, which may not be as effective in handling complex scenes with volumes and surfaces.

How does this method compare to traditional denoising techniques in terms of performance and quality?

The article does not provide a direct comparison between this method and traditional denoising techniques. It would be interesting to see a side-by-side evaluation of the two approaches to understand their relative strengths and weaknesses.

What are the computational requirements of implementing this joint denoising method in real-time applications?

The article does not delve into the computational aspects of implementing this method in real-time scenarios. Understanding the computational overhead and hardware requirements would be crucial for assessing the practicality of this technique in various applications.

Original Abstract Submitted

Denoising images rendered using Monte Carlo sampled ray tracing is an important technique for improving the image quality when low sample counts are used. Ray traced scenes that include volumes in addition to surface geometry are more complex, and noisy when low sample counts are used to render in real-time. Joint neural denoising of surfaces and volumes enables combined volume and surface denoising in real time from low sample count renderings. At least one rendered image is decomposed into volume and surface layers, leveraging spatio-temporal neural denoisers for both the surface and volume components. The individual denoised surface and volume components are composited using learned weights and denoised transmittance. A surface and volume denoiser architecture outperforms current denoisers in scenes containing both surfaces and volumes, and produces temporally stable results at interactive rates.