Abstract: 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.

Abstract: A cache streaming apparatus and method for machine learning. For example, one embodiment of an apparatus comprises: a plurality of compute units to perform machine learning operations; a cache subsystem comprising a hierarchy of cache levels, at least some of the cache levels shared by two or more of the plurality of compute units; and data streaming hardware logic to stream machine learning data in and out of the cache subsystem based on the machine learning operations, the data streaming hardware logic to load data into the cache subsystem from memory before the data is needed by a first portion of the machine learning operations and to ensure that results produced by the first portion of machine learning operations are maintained in the cache subsystem until used by a second portion of the machine learning operations.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus to facilitate trainable visual quality metrics for measuring rendering quality in a graphics environment is disclosed. The apparatus includes a set of processing resources configured to perform a supersampling anti-aliasing operation, the set of processing resources including circuitry to: receive, at a trained visual quality neural network, a sampled signal for visual quality measurement, wherein the sampled signal comprises a reference image of current frame of a video, and wherein the reference image comprising an original image prior to a reconstruction process being applied to the original image; generate, by the trained visual quality neural network, a prediction of a visual quality score for the reconstructed image corresponding to the sampled signal; and notify, by the trained visual quality neural network, a visual quality measurement system of the prediction of the visual quality score for the reconstructed image.

Abstract: 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.

Abstract: Method for real-time computing of indirect illumination in a computer graphics image of a scene, comprising: defining a light propagation volume as a three-dimensional grid consisting of a plurality of grid cells; allocating a plurality of secondary light sources to grid cells of said light propagation volume; accumulating directional, colored irradiance contributions of all secondary light sources in a respective grid cell and repeating this step of accumulating for all grid cells of the light propagation volume to thereby obtain an initial indirect light intensity distribution. The propagation of light in said light propagation volume is then iteratively computed starting with the initial indirect light intensity to obtain a final indirect light intensity distribution, which is used for rendering the scene illuminated by the final indirect light intensity distribution during a usual scene rendering process from a camera's point of view.

Abstract: Method for real-time computing of indirect illumination in a computer graphics image of a scene, comprising: defining a light propagation volume as a three-dimensional grid consisting of a plurality of grid cells; allocating a plurality of secondary light sources to grid cells of said light propagation volume; accumulating directional, colored irradiance contributions of all secondary light sources in a respective grid cell and repeating this step of accumulating for all grid cells of the light propagation volume to thereby obtain an initial indirect light intensity distribution. The propagation of light in said light propagation volume is then iteratively computed starting with the initial indirect light intensity to obtain a final indirect light intensity distribution, which is used for rendering the scene illuminated by the final indirect light intensity distribution during a usual scene rendering process from a camera's point of view.