Application (pre-grant publication)
ADAPTIVE POLYNOMIAL RENDERING
- Number
- 20180040160
- Published
- 2018-02-08
- Filed
- 2016-08-05
- Assignee
- Disney Enterprises, Inc.
- Inventors
- Moon; Bochang; Gross; Markus; McDonagh; Steven; Mitchell; Kenneth J.
- CPC
- G06T15/06
- Verdict
- Low Notable software
- Source
- Google Patents · FreePatentsOnline
The keeper's note
Adaptive polynomial rendering.
Abstract
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.
Background
BACKGROUND
Monte Carlo (MC) ray tracing is a rendering algorithm for synthesizing photo-realistic images from 3D models. MC rendering algorithm can typically be used to simulate a variety of rendering effects through a unified framework, i.e., ray tracing. The rendering time of MC ray tracing, however in many cases, is unacceptable since it typically requires integrating a huge number of ray samples, e.g., more than 10K ray samples per pixel, to generate a converged rendered image.
In attempting to tackle the aforementioned ray performance problem of MC ray tracing, various adaptive rendering techniques have been developed. Those techniques typically involve locally controlled sampling rate to adaptively adjust the image reconstruction. For example, some of those techniques involve adaptive sampling the heterogeneous noise property of rendered images to guide irregular sampling density rather than a uniform sampling, and locally controlling smoothing by considering MC noise so that high-frequency edges are properly preserved.
Adaptive image rendering has a long history. For example, Kajiya, J. T, in the paper entitled “the rendering equation” [ACM SIGGRAPH '86, 143-150], presented a general idea that high-dimensional MC samples can be allocated adaptively in a hierarchical structure by using the variances of the samples and these samples can be integrated to generate rendered images. Approaches using those techniques typically show high-quality rendering res