Intel Corporation

Anton Kaplanyan

Tobias Zirr

Dmitry Kozlov

Dmitry Tarakanov

Gabor Liktor

Sungye Kim

Manu Mathew Thomas

Aleksei Chernigin

Joint denoising and supersampling of graphics data is described. An example of a graphics processor includes multiple processing resources, including a least a first processing resource including a pipeline to perform a supersampling operation; and the pipeline including circuitry to jointly perform denoising and supersampling of received ray tracing input data, the circuitry including first circuitry to receive input data associated with an input block for a neural network, second circuitry to perform operations associated with a feature extraction and kernel prediction network of the neural network, and third circuitry to perform operations associated with a filtering block of the neural network.

Joint denoising and supersampling of graphics data

A graphics processor is provided that includes circuitry configured to generate auxiliary motion vectors for higher-order light-based effects such as shadows, objects reflecting in mirrors, waves in water or other liquids, glossy surfaces, or objects visible through transparent and/or refractive glass. The circuitry is configured to apply light path constraints to simplify the calculations used to generate the auxiliary motion vectors.

Temporal gradients of higher order effects to guide temporal accumulation

An apparatus to facilitate augmenting temporal anti-aliasing with a neural network for history validation is disclosed. The apparatus includes a set of processing resources configured to perform augmented temporal anti-aliasing (TAA), the set of processing resources including circuitry configured to: receive, at a history validation neural network, inputs for a current pixel of a current frame and a reprojected pixel corresponding to the current pixel, the reprojected pixel originating from history data of the current frame; generate, using an output of the history validation neural network, a validated color for the current pixel based on current color data corresponding to the current pixel and history color data corresponding to the reprojected pixel; render an output frame using the validated color; and add the output frame to the history data.

Augmenting temporal anti-aliasing with a neural network for history validation

A graphics processor is provided that includes circuitry configured to facilitate correspondence finding for higher-order light-based effects such as shadows, objects reflecting in mirrors, waves in water or other liquids, glossy surfaces, or objects visible through transparent and/or refractive glass. The circuitry is configured to procedurally generate temporally stable tracking data for transparent and reflective surfaces during rendering of successive frames, hierarchically analyze the successive frames to detect the procedurally generated data within the successive frames, generate residual motion vectors based on the hierarchical analysis, and warp and align a frame and a successively rendered frame based on renderer supplied motion vectors and the residual motion vectors.

Augmenting motion vectors via procedural shader output

An apparatus to facilitate combined denoising and upscaling network with importance sampling in a graphics environment is disclosed. The apparatus includes set of processing resources including circuitry configured to: receive, at an input of a density map neural network, a sampled signal of a current frame and a reconstructed sample of the current frame; output, from the density map neural network, a prediction of a density map of samples based on the input of the current frame; provide the density map of samples to a sampler; reproject the density map of samples to a next frame; and apply the reprojected density map of samples to the next frame to generate a next sampled signal.

Growing community of inventors

Mercer Island, WA, United States of America

Anton Kaplanyan