公开(公告)号：US11941743B2

公开(公告)日：2024-03-26

申请号：US17813709

申请日：2022-07-20

Applicant: NVIDIA Corporation

Inventor： Matthew Milton Pharr

IPC: G06T15/00 ,  G06F9/38 ,  G06T11/40 ,  G06T15/06 ,  G06T15/08

CPC classification number: G06T15/005 ,  G06F9/3877 ,  G06T11/40 ,  G06T15/06 ,  G06T15/08

Abstract: A system and method for generating a set of samples stratified across two-dimensional elementary intervals of a two-dimensional space is disclosed within the application. A computer-implemented technique for generating the set of samples includes selecting an elementary interval associated with a stratification of the two-dimensional space, initializing at least one data structure that indicates valid regions within the elementary interface based on other samples previously placed within the two-dimensional space, and generating a sample in a valid region of the elementary interval utilizing the at least one data structure to identify the valid region prior to generating the sample. In some embodiments, the data structures comprise a pair of binary trees. The process can be repeated for each elementary interval of a selected stratification to generate the set of stratified two-dimensional samples.

公开(公告)号：US20220358708A1

公开(公告)日：2022-11-10

申请号：US17813709

申请日：2022-07-20

Applicant: NVIDIA Corporation

Inventor： Matthew Milton Pharr

IPC: G06T15/00 ,  G06T15/06 ,  G06F9/38 ,  G06T15/08 ,  G06T11/40

Abstract: A system and method for generating a set of samples stratified across two-dimensional elementary intervals of a two-dimensional space is disclosed within the application. A computer-implemented technique for generating the set of samples includes selecting an elementary interval associated with a stratification of the two-dimensional space, initializing at least one data structure that indicates valid regions within the elementary interface based on other samples previously placed within the two-dimensional space, and generating a sample in a valid region of the elementary interval utilizing the at least one data structure to identify the valid region prior to generating the sample. In some embodiments, the data structures comprise a pair of binary trees. The process can be repeated for each elementary interval of a selected stratification to generate the set of stratified two-dimensional samples.

公开(公告)号：US11430172B2

公开(公告)日：2022-08-30

申请号：US16714338

申请日：2019-12-13

Applicant: NVIDIA Corporation

Inventor： Matthew Milton Pharr

IPC: G06T15/00 ,  G06T15/06 ,  G06F9/38 ,  G06T15/08 ,  G06T11/40

Abstract: A system and method for generating a set of samples stratified across two-dimensional elementary intervals of a two-dimensional space is disclosed within the application. A computer-implemented technique for generating the set of samples includes selecting an elementary interval associated with a stratification of the two-dimensional space, initializing at least one data structure that indicates valid regions within the elementary interface based on other samples previously placed within the two-dimensional space, and generating a sample in a valid region of the elementary interval utilizing the at least one data structure to identify the valid region prior to generating the sample. In some embodiments, the data structures comprise a pair of binary trees. The process can be repeated for each elementary interval of a selected stratification to generate the set of stratified two-dimensional samples.

公开(公告)号：US20240371072A1

公开(公告)日：2024-11-07

申请号：US18413564

申请日：2024-01-16

Applicant: NVIDIA Corporation

Inventor： Bartlomiej Wronski ,  Matthew Milton Pharr ,  Marco Salvi

IPC: G06T15/04

Abstract: Stochastic texture filtering introduces randomness into texel sampling and/or filtering. Instead of computing a closest texel for the texture coordinates, randomness is introduced by stochastic sampling to obtain one texel. Stochastic sampling is also applied for filtering the texels when multiple samples are used and/or to perform temporal filtering. A first technique is used for discrete filters and filter-specific sample weights are generated. In contrast with conventional techniques, the sample weights are not applied directly to the single texel value. The single texel is randomly selected for each pixel, with probability proportional to an associated sample weight. A second technique is used for continuous filters and weights are not generated. Instead, the texture coordinates are perturbed with a random offset, which is drawn from a filter-specific probability distribution. Stochastic texture filtering improves the performance of texture filtering in terms of speed and quality and is compatible with image reconstruction techniques.

公开(公告)号：US11055381B1

公开(公告)日：2021-07-06

申请号：US16900046

申请日：2020-06-12

Applicant: NVIDIA Corporation

Inventor： David Augustus Hart ,  Matthew Milton Pharr ,  Thomas Müller ,  Ward Lopes ,  Morgan McGuire ,  Peter Schuyler Shirley

IPC: G06T11/00 ,  G06F17/17 ,  G06T3/00 ,  G06N3/08 ,  G06N3/04

Abstract: Sampling a function is used for many applications, such as rendering images. The challenge is how to select the best samples to minimize computations and produce accurate results. An alternative is to use a larger number of samples that may not be carefully selected in an attempt to increase accuracy. For a function that is an integral, such as functions used to render images, a sample distribution may be computed by inverting the integral. Unfortunately, for many integrals, it is neither easy nor practical to compute the inverted integral. Instead, warp functions may be combined to provide a sample distribution that accurately approximates the factors of the product being integrated. Each warp function approximates an inverted term of the product while accounting for the effects of warp functions approximating other factors in the product. The selected warp functions are customized or “fitted” to implement importance sampling for the approximated product.