Sample distribution-informed denoising and rendering

A method comprising:

• on a graphics processor device:
• receiving, at an input block of a neural network model, a set of data including previous frame data, current frame data, velocity data, and jitter offset data;
• pre-processing the set of data to generate pre-processed data;
• providing first pre-processed data to a feature extraction network of the neural network model and second pre-processed data to an output block of the neural network model, the first pre-processed data in a datatype having a first precision and the second pre-processed data in a datatype having a second precision that is higher than the first precision;
• processing the pre-processed data at the feature extraction network via one or more encoder stages and one or more decoder stages;
• outputting tensor data from the feature extraction network to the output block;
• and
• generating an output image via an output block of the neural network model, wherein the output image is a denoised, supersampled, and anti-aliased output image and the output block is configured to filter the output image based on sample reliability metrics computed based on sample distribution data for samples within the current frame data.

A non-transitory machine readable medium storing instructions which, when executed by one or more processors including a graphics processor, cause the one or more processors to perform operations comprising:

• receiving, at an input block of a neural network model, a set of data including previous frame data, current frame data, velocity data, jitter offset data, and sample reliability metrics determined based on sample distribution data for samples within the current frame data;
• pre-processing the set of data to generate pre-processed data;
• providing first pre-processed data to a feature extraction network of the neural network model and second pre-processed data to an output block of the neural network model, the first pre-processed data in a datatype having a first precision and the second pre-processed data in a datatype having a second precision that is higher than the first precision;
• processing the pre-processed data at the feature extraction network via one or more encoder stages and one or more decoder stages;
• outputting tensor data from the feature extraction network to the output block;
• and
• generating an output image via an output block of the neural network model, wherein the output image is a denoised, supersampled, and anti-aliased output image and the output block is configured to filter the output image based on the sample reliability metrics.