Disney Enterprises, Inc.

Pixar

Bochang Moon

Kenneth John Mitchell

Jose A Iglesias-Guitian

Steven G McDonagh

Markus H Gross

Charalampos Koniaris

Kenneth Mitchell

Eric Smolikowski

Steven McDonagh

Techniques for selectively removing Monte Carlo (MC) noise from a geometric buffer (G-buffer). Embodiments identify the G-buffer for rendering an image of a three-dimensional scene from a viewpoint. Embodiments determine, for each of a plurality of pixels in the image being rendered, respective world position information based on the three-dimensional scene and a position and orientation of the viewpoint. A pre-filtering operation is then performed to selectively remove the MC noise from the G-buffer, based on the determined world position information for the plurality of pixels.

Noise reduction on G-buffers for Monte Carlo filtering

Views of a virtual space may be presented based on predicted colors of individual pixels of individual frame images that depict the views of the virtual space. Predictive models may be assigned to individual pixels that predict individual pixel colors of individual pixels at individual time points. Individual models may be updated and/or reprojected to other pixels based on comparisons of the predicted pixel colors and colors specified by in a raster input signal.

System and method of presenting views of a virtual space

Embodiments can provide adaptive image filtering. Under this approach, image quality can be enhanced by adjusting an approximation function to better adapt image signals in different parts of an image. Certain parts of the image may be enhanced using a certain approximation function while some other parts of the image may be enhanced using a different approximation function. In certain embodiments, the approximation function selected for a part of the image can be a polynomial function having a specific order. The specific polynomial order can be applied directly to obtain an estimated image value of the part of the image. In certain embodiments, the estimation of the reconstruction error can include iteratively estimating a bias term of the reconstruction error and a variance term of the reconstruction error.

Adaptive polynomial rendering

Systems, methods and articles of manufacture for rendering an image. Embodiments include selecting a plurality of positions within the image and constructing a respective linear prediction model for each selected position. A respective prediction window is determined for each constructed linear prediction model. Additionally, embodiments render the image using the linear prediction models using the constructed linear prediction models, where at least one of the constructed linear prediction models is used to predict values for two or more of a plurality of pixels of the image, and where a value for at least one of the plurality of pixels is determined based on two or more of the constructed linear prediction models.

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Edinburgh, United Kingdom

Bochang Moon