Intel corporation (20240119558). TEMPORALLY AMORTIZED SUPERSAMPLING USING A KERNEL SPLATTING NETWORK simplified abstract

TEMPORALLY AMORTIZED SUPERSAMPLING USING A KERNEL SPLATTING NETWORK

Organization Name

intel corporation

Inventor(s)

Dmitry Kozlov of Nizhny Novgorod (RU)

Aleksei Chernigin of Nizhny Novgorod (RU)

Dmitry Tarakanov of Nizhny Novgorod (RU)

TEMPORALLY AMORTIZED SUPERSAMPLING USING A KERNEL SPLATTING NETWORK - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240119558 titled 'TEMPORALLY AMORTIZED SUPERSAMPLING USING A KERNEL SPLATTING NETWORK

Simplified Explanation

The abstract describes a graphics processor that uses a mixed precision convolutional neural network to perform supersampling anti-aliasing operations. The processor receives data including previous frame data, current frame data, jitter offset data, and velocity data, preprocesses it, passes it through a feature extraction network, and generates an anti-aliased output frame based on the processed data.

• Processing resources configured for supersampling anti-aliasing operation
• Utilizes a mixed precision convolutional neural network
• Receives and preprocesses data including previous and current frame data
• Passes data through feature extraction network with encoder and decoder stages
• Generates anti-aliased output frame based on processed data

Potential Applications

The technology described in this patent application could be applied in various industries such as gaming, virtual reality, augmented reality, and image processing.

Problems Solved

This technology addresses the issue of aliasing in graphics processing, which can result in jagged edges and visual artifacts in images and videos.

Benefits

The benefits of this technology include improved image quality, reduced visual artifacts, and enhanced graphics performance in applications that require anti-aliasing.

Potential Commercial Applications

Potential commercial applications of this technology include gaming consoles, graphics cards, virtual reality headsets, and image processing software.

Possible Prior Art

One possible prior art related to this technology is the use of neural networks for image processing and anti-aliasing in graphics processing units.

How does this technology compare to traditional anti-aliasing methods?

This article does not provide a direct comparison between this technology and traditional anti-aliasing methods. A comparison of performance, image quality, and computational efficiency would be helpful in understanding the advantages of this approach.

What are the limitations of using a mixed precision convolutional neural network for anti-aliasing?

The article does not discuss any potential limitations or challenges associated with using a mixed precision convolutional neural network for anti-aliasing operations. Understanding the constraints of this approach could provide insights into its practical applications and potential improvements.

Original Abstract Submitted

one embodiment provides a graphics processor comprising a set of processing resources configured to perform a supersampling anti-aliasing operation via a mixed precision convolutional neural network. the set of processing resources include circuitry configured to receive, at an input block of a neural network model, a set of data including previous frame data, current frame data, jitter offset data, and velocity data, pre-process the set of data to generate pre-processed data, provide pre-processed data to a feature extraction network of the neural network model and an output block of the neural network model, process the first pre-processed data at the feature extraction network via one or more encoder stages and one or more decoder stages, output tensor data from the feature extraction network to the output block, and generate an anti-aliased output frame via the output block based on the current frame data and the tensor data output from the feature extraction network.