UC Santa Barbara researchers have evaluated the complex relationship between the filter parameters and noisy scene data using a nonlinear regression model, a multilayer perceptron neural network.
Producing photo-realistic images from a model requires taking an intricate multi-dimensional integral of the scene function at every pixel of the image. Generating effects like depth-of-field and motion blur requires computing the integral over different domains, such as lens position and time. Monte Carlo rendering systems approximate this integral by sending light rays (e.g. samples) in the multidimensional space to evaluate the scene function, but the inaccuracies of this approximation appear as “noise” in the resulting image.The most successful approaches in Monte Carlo noise reduction utilize additional scene features such as world position, shading normal, and texture to filter the image. The main challenge with such methods lies in finding the appropriate weights for each feature in the filter.
UC Santa Barbara researchers have evaluated the complex relationship between the filter parameters and noisy scene data using a nonlinear regression model, a multilayer perceptron neural network. The network is combined with a filter and trained in an offline process on noisy images of scenes with a variety of Monte Carlo effects. This produces filtered images that are superior to previous approaches in terms of depth-of-field, volumetric rendering, area light sources, and global illumination.
|United States Of America||Issued Patent||10,192,146||01/29/2019||2015-002|
|United States Of America||Published Application||US190122076||04/25/2019||2015-002|
monte carlo, computer graphics, path tracing, noise reduction, depth-of-field, neural network, nonlinear regression model, indtelecom