IP Library › Granted Patent US 12,299,801
Granted Patent B2
US 12,299,801 · App. 17/031,693 · Granted May 13, 2025

Grid-based light sampling for ray tracing applications

Inventors: Jakub Boksansky (Munich, DE); Paula Eveliina Jukarainen (Espoo, FI); Christopher Ryan Wyman (Redmond, WA)
Assignee: NVIDIA Corporation
G06T15/005G06T15/06
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,299,801
App. No.
17/031,693
Granted
May 13, 2025
Kind
B2
Abstract

Devices, systems, and techniques to incorporate lighting effects into computer-generated graphics. In at least one embodiment, a virtual scene comprising a plurality of lights is rendered by subdividing the virtual area and stored, in a record corresponding to a subdivision of the virtual area, information indicative of one or more lights in the virtual area selected based on a stochastic model. Pixels near a subdivision are rendered based on the light information stored in the subdivision.

Claims (48)

1. A system, comprising:

at least one processor; and

at least one memory comprising stored instructions that, in response to execution by the at least one processor, cause the system to at least:

sample a first set of lights from among a plurality of lights in each of a plurality of subdivisions of a virtual area, wherein each of the first set of lights is sampled from a corresponding subdivision of the plurality of subdivisions based at least in part on a contribution of the first set of lights in lighting the corresponding subdivision;

store information indicative of the first set of lights sampled from each of the plurality of subdivisions in at least one cell of a data structure, wherein each cell of the data structure corresponds to a subdivision of the virtual area;

sample a second set of lights from a subset of the first set of lights in each cell of the data structure; and

use the second set of lights to render a pixel of an image of the virtual area.

2. The system of claim 1 , wherein each of the plurality of subdivisions of the virtual area is a uniform subdivision of the virtual area.

3. The system of claim 2 , wherein each of the plurality of subdivisions are defined to form a grid encompassing the virtual area.

4. The system of claim 1 , wherein the first set of lights is sampled based, at least in part, on a probability proportional to the contribution of the first set of lights to lighting in each of the plurality of subdivisions.

5. The system of claim 1 , the at least one memory comprising stored instructions that, in response to execution by the at least one processor, cause the system to at least:

sample the first set of lights based on a probability density function, wherein the probability density function is based, at least in part, on intensity of the first set of lights and distance between the first set of lights and each of the plurality of subdivisions.

6. The system of claim 1 , wherein the first set of lights is sampled by one of a plurality of threads executed in parallel by a graphics processing unit.

7. The system of claim 1 , wherein the pixel is rendered by at least identifying one or more subdivisions proximate to the pixel and obtaining, from one or more cells of the data structure corresponding to the one or more subdivisions, information indicative of the second set of lights.

8. The system of claim 7 , wherein the pixel is rendered based at least in part on a number of lights from each of the plurality of subdivisions, wherein the number is inversely proportional to distance between each of the plurality of subdivisions and the pixel.

9. A non-transitory machine-readable medium having stored thereon instructions which, in response to execution by one or more processors, cause the one or more processors to at least:

sample a first set of lights from among a plurality of lights in each of a plurality of subdivisions of a virtual area, wherein each of the first set of lights is sampled from a corresponding subdivision of the plurality of subdivisions based at least in part on a contribution of the first set of lights in lighting the corresponding subdivision;

store information indicative of the first set of lights sampled from each of the plurality of subdivisions in at least one cell of a data structure, wherein each cell of the data structure corresponds to a subdivision of the virtual area;

sample a second set of lights from a subset of the first set of lights in each cell of the data structure;

and

use the second set of lights to render a pixel of an image of the virtual area.

10. The non-transitory machine-readable medium of claim 9 , wherein each of the plurality of subdivisions of the virtual area is a uniform subdivision of the virtual area.

11. The non-transitory machine-readable medium of claim 9 , having stored thereon instructions which, in response to execution by one or more processors, cause the one or more processors to at least:

sample the first set of lights based, at least in part, on a probability distribution that is based, at least in part, on the contribution of the first set of lights to lighting in each of the plurality of subdivisions.

12. The non-transitory machine-readable medium of claim 9 , having stored thereon instructions which, in response to execution by one or more processors, cause the one or more processors to at least:

sample the first set of lights based, at least in part, on a probability distribution that is based, at least in part, on distance between the first set of lights and each of the plurality of subdivisions.

13. The non-transitory machine-readable medium of claim 9 , having stored thereon instructions which, in response to execution by one or more processors, cause the one or more processors to at least:

sample the first set of lights from the at least one cell of the data structure based, at least in part, on using resampled importance sampling.

14. The non-transitory machine-readable medium of claim 9 , having stored thereon instructions which, in response to execution by one or more processors, cause the one or more processors to at least:

sample the plurality of lights, including the first set of lights, by at least executing, in parallel, a corresponding number of one or more threads on a graphics processing unit.

15. The non-transitory machine-readable medium of claim 9 , having stored thereon instructions which, in response to execution by one or more processors, cause the one or more processors to at least:

render the pixel by at least identifying one or more subdivisions proximate to the pixel and obtaining, from one or more cells of the data structure corresponding to the one or more subdivisions, information indicative of the second set of lights stored in the one or more cells of the data structure.

16. The non-transitory machine-readable medium of claim 15 , having stored thereon instructions which, in response to execution by one or more processors, cause the one or more processors to at least:

obtain, from a cell of the data structure, information indicative of a number of lights inversely proportional to distance between each of the plurality of subdivisions and the pixel.

17. A method, comprising:

defining a plurality of subdivisions of a virtual area;

sampling a first set of lights from among a plurality of lights in each of a plurality of subdivisions of a virtual area, wherein each of the first set of lights is sampled from a corresponding subdivision of the plurality of subdivisions based at least in part on a contribution of the first set of lights in lighting the corresponding subdivision;

storing information indicative of the first set of lights sampled from each of the plurality of subdivisions in at least one cell of a data structure, where each cell of the data structure corresponds to a subdivision of the virtual area;

sampling a second set of lights from a subset of the first set of lights in each cell of the data structure; and

use the second set of lights to render a pixel of an image of the virtual area.

18. The method of claim 17 , wherein the plurality of subdivisions comprise cells of a grid.

19. The method of claim 17 , wherein the first set of lights is sampled based, at least in part, on a probability distribution indicative of a contribution of the first set of lights to lighting in each of the plurality of subdivisions.

20. The method of claim 17 , wherein the first set of lights is sampled based, at least in part, on a distance between at least one light of the first set of lights and each of the plurality of subdivisions.

21. The method of claim 17 , wherein the first set of lights is sampled by executing a thread on a graphics processing unit.

22. The method of claim 17 , further comprising:

rendering the pixel by at least identifying one or more subdivisions encompassing a region around the pixel.

23. The method of claim 22 , further comprising:

selecting samples of the second set of lights to use to render the pixel, the second set of lights selected from one or more cells of the data structure corresponding to the one or more subdivisions based, at least in part, on distance between the identified one or more subdivisions and the pixel.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 13, 2020
From: BOKSANSKY, JAKUB; JUKARAINEN, PAULA EVELIINA; WYMAN, CHRISTOPHER RYAN
To: NVIDIA CORPORATION
Reel/Frame 054043/0858 →
Continuity (2)
Provisional Application 63068906 · Aug 21, 2020
Related Publication 20220058851A1 · Feb 24, 2022
References Cited (147)
US 6078332A · Ohazama · 2000 [cited by applicant]
US 7696995B2 · McTaggart · 2010 [cited by examiner]
US 10157494B2 · Ha · 2018 [cited by examiner]
US 10902670B1 · Schied · 2021 [cited by examiner]
US 20060251325A1 · Florin · 2006 [cited by examiner]
US 20130002694A1 · Ha · 2013 [cited by examiner]
US 20130120384A1 · Jarosz · 2013 [cited by examiner]
US 20140342823A1 · Kapulkin · 2014 [cited by examiner]
US 20150022524A1 · Ahn · 2015 [cited by examiner]
US 20160042559A1 · Seibert et al. · 2016 [cited by applicant]
US 20160071308A1 · Nichols · 2016 [cited by examiner]
US 20160125643A1 · Tokuyoshi · 2016 [cited by examiner]
US 20160171754A1 · Ahn · 2016 [cited by examiner]
US 20160343161A1 · Paladini et al. · 2016 [cited by applicant]
US 20180130252A1 · Seibert et al. · 2018 [cited by applicant]
US 20180174354A1 · Dufay · 2018 [cited by examiner]
US 20180374260A1 · Koylazov · 2018 [cited by examiner]
US 20190325640A1 · Jiddi · 2019 [cited by examiner]
CN 103729873A · 2014 [cited by applicant]
CN 111260766 · 2020 [cited by examiner]
EP 2827302A2 · 2015 [cited by examiner]
EP 3340180A1 · 2018 [cited by applicant]
EP 3399502A1 · 2018 [cited by examiner]
Boksansky et al., “Rendering Many Lights with Grid-Based Reservoirs: Next Generation Real-Time Rendering with DXR, Vulkan, and OptiX,” NVIDIA, Aug. 23, 2021, 15 pages. [cited by applicant]
Boksansky et al., “Rendering of Many Lights with Grid-Based Reservoirs,” ACM Siggraph Symposium on Interactive 3D Graphics and Games Online, Apr. 20-22, 2021, 1 pages. [cited by applicant]
Fernandez et al., “Local Illumination Environments for Direct Lighting Acceleration,” Thirteenth Eurographics Workshop on Rendering, Jan. 1, 2002, 8 pages. [cited by applicant]
International Search Report and Written Opinion for Application No. PCT/US2021/046785, mailed Nov. 24, 2021, filed Aug. 19, 2021, 18 pages. [cited by applicant]
Lin et al., “Real-Time Stochastic Lightcuts,” Proceedings of the ACM on Computer Graphics and Interactive Techniques, 3(1): Apr. 18, 2020, 18 pages. [cited by applicant]
Moreau et al., “Importance Sampling of Many Lights on the GPU,” NVIDIA, Jan. 1, 2019, 29 pages. [cited by applicant]
Koskela et al., “Blockwise Multi-Order Feature Regression for Real-Time Path-Tracing Reconstruction,” ACM Transactions on Graphics, 38(5): Jun. 2019, 14 pages. [cited by applicant]
Krivánek et al., “Making Radiance and Irradiance Caching Practical: Adaptive Caching and Neighbor Clamping,” Eurographics Symposium on Rendering, 2006, 12 pages. [cited by applicant]
Krivánek et al., “Radiance Caching for Efficient Global Illumination Computation,” IEEE Transactions on Visualization and Computer Graphics, 11(5): 2005, 12 pages. [cited by applicant]
Lafortune et al., “Bi-Directional Path Tracing,” International Conference on Computational Graphics and Visualization Techniques Compugraphics, vol. 93, 1993, 8 pages. [cited by applicant]
Lai et al., “Photorealistic Image Rendering with Population Monte Carlo Energy Redistribution,” Eurographics Symposium on Rendering Techniques, 1981, 9 pages. [cited by applicant]
Lehtinen et al., “Gradient-Domain Metropolis Light Transport,” ACM Transactions on Graphics, 32(4): Jul. 2013, 11 pages. [cited by applicant]
Lehtinen et al., “Reconstructing the Indirect Light Field for Global Illumination,” ACM Transactions on Graphics, 31(4): Jul. 2012, 10 pages. [cited by applicant]
Lehtinen et al., “Temporal Light Field Reconstruction for Rendering Distribution Effects,” SIGGRAPH, 30(4): Jul. 2011, 12 pages. [cited by applicant]
Li et al., “Anisotropic Gaussian Mutations for Metropolis Light Transport through Hessian-Hamiltonian Dynamics,” SIGGRAPH Asia, 34(6): Oct. 2015, 13 pages. [cited by applicant]
Lin et al., “Real-Time Rendering with Lighting Grid Hierarchy,” Proceedings of I3D, 2(1): Jun. 2019, 10 pages. [cited by applicant]
Mara et al., “An Efficient Denoising Algorithm for Global Illumination,” Proceedingd of HPG, Jul. 28-30, 2017, 7 pages. [cited by applicant]
Mickey, “Some Finite Population Unbiased Ratio and Regression Estimators,” Journal of the American Statistical Association, 54, 287 Sep. 1959, 19 pages. [cited by applicant]
Moon et al., “Adaptive Polynomial Rendering,” ACM SIGGRAPH , 35(4): Jul. 2016, 10 pages. [cited by applicant]
Moon et al., “Adaptive Rendering Based on Weighted Local Regression,” ACM Transactions on Graphics, 33(5): Sep. 2014, 13 pages. [cited by applicant]
Moon et al., “Adaptive Rendering with Linear Predictions,” SIGGRAPH 34(4): Jul. 2015, 11 pages. [cited by applicant]
Moreau et al., “Dynamic Many-Light Sampling for Real-Time Ray Tracing,” Proceedings of High-Performance Graphics, 2019, 6 pages. [cited by applicant]
Müller et al., “Practical Path Guiding for Efficient Light-Transport Simulation,” Eurographics Symposium on Rendering, 36(4): Jun. 2017, 10 pages. [cited by applicant]
NVIDIA Research, “NVIDIA OptiX AI-Accelerated Denoiser,” 2017, 11 pages. [cited by applicant]
Olsson et al., “Tiled Shading,” Journal of Graphics, GPU, Tools, 15(4): 2011, 16 pages. [cited by applicant]
Otsu et al., “Geometry-Aware Metropolis Light Transport,” Proceedings of SIGGRAPH Asia, 37(6): 2018, 11 pages. [cited by applicant]
Otto et al., “Unbiased Ratio Estimators,” Nature, 174(4423): Aug. 1954, pp. 270-271. [cited by applicant]
Ou et al., “LightSlice: Matrix Slice Sampling for the Many-Lights Problem,” SIGGRAPH Asia, 30(6): Dec. 2011, 8 pages. [cited by applicant]
Pajot et al., “Combinatorial Bidirectional Path-Tracing for Efficient Hybrid CPU/GPU Rendering,” Eurographics, 30(2): 2011, 10 pages. [cited by applicant]
Parker et al., “OptiX: A General Purpose Ray Tracing Engine,” ACM Transactions on Graphics, 29(4): Jul. 2010, 13 pages. [cited by applicant]
Pegoraro et al., “Sequential Monte Carlo Adaptation in Low-Anisotropy Participating Media,” Eurographics Symposium on Rendering, 27(4): 2008, 8 pages. [cited by applicant]
Popov et al., “Probabilistic Connections for Bidirectional Path Tracing,” Eurographics Symposium on Rendering, 34(4): 2015, 12 pages. [cited by applicant]
Powell et al., “Weighted Uniform Sampling—a Monte Carlo Technique for Reducing Variance,” IMA Journal of Applied Mathematics, 2(3): Sep. 1966, 9 pages. [cited by applicant]
Rao et al., A Monte Carlo Study of Some Ratio Estimators, Sankhya: The Indian Journal of Statistics, 1967, 11 pages. [cited by applicant]
Rousselle et al., “Adaptive Rendering with Non-Local Means Filtering,” ACM Transactions on Graphics, 31(6): Nov. 2012, 12 pages. [cited by applicant]
Rousselle et al., “Adaptive Sampling and Reconstruction Using Greedy Error Minimization,” ACM Transactions on Graphics, 30(6): Dec. 2011, 11 pages. [cited by applicant]
Rousselle et al., “Image-Space Control Variates for Rendering,” ACM Transactions on Graphics, 35(6): Nov. 2016, 12 pages. [cited by applicant]
Rousselle et al., “Robust Denoising Using Feature and Color Information,” Proceedings of Pacific Graphics, 32(7): Oct. 2013, 10 pages. [cited by applicant]
Rubin, “Comment,” Journal of the American Statistical Association 82, 398 Jun. 1987, pp. 543-546. [cited by applicant]
Sbert et al., “Real-Time Light Animation,” Eurographics, 23(3): 2004, 9 pages. [cited by applicant]
Schied et al., “Gradient Estimation for Real-Time Adaptive Temporal Filtering,” Proceedings of the ACM on Computer Graphics and Interactive Techniques, 1(2): Aug. 2018, 16 pages. [cited by applicant]
Schied et al., “Spatiotemporal Variance-Guided Filtering: Real-Time Reconstruction for Path-Traced Global Illumination,” In Proceedings of HPG, 2017, 12 pages. [cited by applicant]
Schied, “Video Series: Path Tracing for Quake II in Two Months,” retrieved from https://devblogs.nvidia.com/path-tracing-quake-ii/, 2019, 15 pages. [cited by applicant]
Schwarzhaupt et al., “Practical Hessian-Based Error Control for Irradiance Caching,” ACM Transactions on Graphics, 31(6): Nov. 2012, 10 pages. [cited by applicant]
Spanier et al., “Quasi-Random Methods for Estimating Integrals Using Relatively Small Samples,” SIAM Review, 36(1): 1994, 27 pages. [cited by applicant]
Spanier,“A New Family of Estimators for Random Walk Problems,” Journal of Applied Mathematics, 23(1): Jan. 1979, 31 pages. [cited by applicant]
Stachowiak, “Stochastic Screen-Space Reflections,” Advances in Real-Time Rendering in Games, Part I, ACM SIGGRAPH Courses, Aug. 2015, 47 pages. [cited by applicant]
Talbot et al., “Importance Resampling for Global Illumination,” Eurographics Symposium on Rendering, 2005, 8 pages. [cited by applicant]
Talbot, “Importance Resampling for Global Illumination,” Masters Thesis, Brigham Young University, Sep. 16, 2005, 88 pages. [cited by applicant]
Tokuyoshi et al., “Hierarchical Russian Roulette for Vertex Connections,” ACM Transactions on Graphics, 38(4): Jul. 2019, 12 pages. [cited by applicant]
Tokuyoshi et al., “Stochastic Light Culling,” Journal of Computer Graphics Techniques, 5(1): 2016, 26 pages. [cited by applicant]
Tomasi et al., “Bilateral Filtering for Gray and Color Images,” International Conference on Computer Vision, 1998, 8 pages. [cited by applicant]
Veach et al., “Bidirectional Estimators for Light Transport,” Eurographics Workshop on Rendering, 1995, 20 pages. [cited by applicant]
Veach et al., “Metropolis Light Transport,” SIGGRAPH, vol. 31, 1997, 12 pages. [cited by applicant]
Vitter, “Random Sampling with a Reservoir,” ACM Transanctions om Mathematical Software, 11(1): 1985, 21 pages. [cited by applicant]
Vogels et al., “Denoising with Kernel Prediction and Asymmetric Loss Functions,” ACM Transactions on Graphics, 37(4): Article 124, 2018, 15 pages. [cited by applicant]
Vorba et al., “On-Line Learning of Parametric Mixture Models for Light Transport Simulation,” ACM Transactions on Graphics, 33(4): Aug. 2014, 11 pages. [cited by applicant]
Vévoda et al., “Bayesian Online Regression for Adaptive Direct Illumination Sampling,” ACM Transactions on Graphics, 37(4): Jul. 2018, 12 pages. [cited by applicant]
Walker, “New Fast Method for Generating Discrete Random Numbers with Arbitrary Frequency Distributions,” Electronics Letters, 10(8): Feb. 26, 1974, 2 pages. [cited by applicant]
Walter et al., “Lightcuts: A Scalable Approach to Illumination,” ACM SIGGRAPH, 24(3): Aug. 2005, 10 pages. [cited by applicant]
Walter et al., “Multidimensional Lightcuts,” ACM Transactions on Graphics, 25(3): Jul. 2006, 8 pages. [cited by applicant]
Ward et al., “A Ray Tracing Solution for Diffuse Interreflection,” Proc. SIGGRAPH 22(4): Aug. 1988, 8 pages. [cited by applicant]
Ward et al., “Irradiance Gradients,” CE_EGWR93, 1992, 17 pages. [cited by applicant]
Ward, “Adaptive Shadow Testing for Ray Tracing,” Eurographics Workshop on Rendering (Focus on Computer Graphics), 1994, 16 pages. [cited by applicant]
Winkelmann, “Short Films by Beeple,” retrieved from https://www.beeple-crap.com/films, 2015, 6 pages. [cited by applicant]
Worthley, “Unbiased Ratio-Type Estimators,” Masters Thesis, 1967, 53 pages. [cited by applicant]
Wyman et al., “Introduction to DirectX Raytracing,” ACM SIGGRAPH Courses, 2018, 4 pages. [cited by applicant]
Wyman, “Exploring and Expanding the Continuum of OIT Algorithms,” High Performance Graphics, 2016, 11 pages. [cited by applicant]
Xu et al., “A New Way to Re-Using Paths,” ICCSA, 2007, vol. 4706, 10 pages. [cited by applicant]
Yuksel, “Stochastic Lightcuts,” High Performance Graphics, 2019, 6 pages. [cited by applicant]
Zwicker et al., “Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering,” Computer Graphics Forum, Proceedings of Eurographics State of the Art Reports, 34(2): May 2015, 15 pages. [cited by applicant]
Bauszat et al., “Gradient-Domain Path Reusing,” Process SIGGRAPH Asia, 36(6): Nov. 2017, 9 pages. [cited by applicant]
Bauszat et al., “Guided Image Filtering for Interactive High-Quality Global Illumination,” Eurographics Symposium on Rendering 30(4): Jun. 2011, 8 pages. [cited by applicant]
Bekaert et al., “Accelerating Path Tracing by Re-Using Paths,” Eurographics Workshop on Rendering, 2002, 10 pages. [cited by applicant]
Bekaert et al., “Weighted Importance Sampling Techniques for Monte Carlo Radiosity,” Eurographics Workshop on Rendering, Jun. 2000, 13 pages. [cited by applicant]
Benty et al., “The Falcor Rendering Framework,” retrieved from https://github.com/NVIDIAGameWorks/Falcor, 2019, 4 pages. [cited by applicant]
Binder et al., “Massively Parallel Path Space Filtering,” Feb. 15, 2019, 6 pages. [cited by applicant]
Bitterli et al., “Nonlinearly Weighted First-Order Regression for Denoising Monte Carlo Renderings,” 35(4): Jun. 2016, 11 pages. [cited by applicant]
Bitterli et al., “Selectively Metropolised Monte Carlo Light Transport Simulation,” ACM Transactions on Graphics, 38(6): Nov. 2019, 10 pages. [cited by applicant]
Bitterli et al., “Spatiotemporal Reservoir Resampling for Real-Time Ray Tracing with Dynamic Direct Lighting,” ACM Transactions on Graphics, Jul. 2020, 17 pages. [cited by applicant]
Buades et al., “A Review of Image Denoising Algorithms, with a New One,” Multiscale Modeling & Simulation, 4(2): Jan. 2005, pp. 490-530. [cited by applicant]
Burke et al., “Bidirectional Importance Sampling for Direct Illumination,” Eurographics Symposium on Rendering, 2005, 11 pages. [cited by applicant]
Burke et al., “Bidirectional Importance Sampling for Illumination from Environment Maps,” In ACM SIGGRAPH Sketches, Oct. 22, 2004, 79 pages. [cited by applicant]
Castro et al., “Efficient Reuse of Paths for Random Walk Radiosity,” Computers & Graphics, 32(1): Feb. 2008, 8 pages. [cited by applicant]
Chaitanya et al., “Interactive Reconstruction of Monte Carlo Image Sequences Using a Recurrent Denoising Autoencoder,” ACM Trans. Graph. 36, 4, Article 98, 2017, 12 pages. [cited by applicant]
Chakravarty et al., “Matrix Bidirectional Path Tracing,” Eurographics Symposium on Rendering—Experimental Ideas & Implementations, 2018, 10 pages. [cited by applicant]
Chao, “A General Purpose Unequal Probability Sampling Plan,” Biometrika 69(3): Dec. 1982, pp. 653-656. [cited by applicant]
Christensen et al., “The Path to Path-Traced Movies,” Foundations and TrendsR in Computer Graphics and Vision 10(2): Oct. 2016, 76 pages. [cited by applicant]
Cline et al., “Energy Redistribution Path Tracing,” SIGGRAPH 24(3): Jul. 2005, 10 pages. [cited by applicant]
Cook, Stochastic Sampling in Computer Graphics, ACM Transactions on Graphics, 5(1): Jan. 1986, 22 pages. [cited by applicant]
Dachsbacher et al., “Scalable Realistic Rendering with Many-Light Methods,” Eurographics 33(1): Feb. 2014, 16 pages. [cited by applicant]
Dammertz et al., Edge-Avoiding À-Trous Wavelet Transform for Fast Global Illumination Filtering, HPG Eurographics Association, Saarbrucken, Germany, 2010, 9 pages. [cited by applicant]
Davidovic et al., “Combining Global and Local Virtual Lights for Detailed Glossy Illumination,” SIGGRAPH Asia, 29(6): Dec. 2010, 8 pages. [cited by applicant]
Deng et al., “Photon Surfaces for Robust, Unbiased Volumetric Density Estimation,” SIGGRAPH, 38(4): Jul. 2019, 12 pages. [cited by applicant]
Donikian et al., “Accurate Direct Illumination Using Iterative Adaptive Sampling,” IEEE Transactions on Visualization Graphics, 12(3): May 2006, pp. 353-364. [cited by applicant]
Efraimidis et al., “Weighted Random Sampling with a Reservoir,” Information Processing Letters, 97(5): Mar. 2006, pp. 181-185. [cited by applicant]
Efraimidis, “Weighted Random Sampling over Data Streams,” Jul. 28, 2015, 14 pages. [cited by applicant]
Estevez et al., “Importance Sampling of Many Lights with Adaptive Tree Splitting,” ACM on Computer Graphics and Interactive Techniques, 1(2): Aug. 2018, 17 pages. [cited by applicant]
Georgiev et al., “Blue-Noise Dithered Sampling,” ACM SIGGRAPH Talks, ACM Press, Jul. 24-28, 2016, 1 page. [cited by applicant]
Ghosh et al., “Sequential Sampling for Dynamic Environment Map Illumination,” Eurographics Symposium on Rendering, 2006, 12 pages. [cited by applicant]
Hachisuka et al., “Multidimensional Adaptive Sampling and Reconstruction for Ray Tracing,” SIGGRAPH, 27(3): Aug. 2008, 10 pages. [cited by applicant]
Hachisuka et al., “Multiplexed Metropolis Light Transport,” SIGGRAPH, 33(4): Jul. 2014, 10 pages. [cited by applicant]
Handscomb, “Remarks on a Monte Carlo Integration Method,” Numerische Mathematik, 6(1): Dec. 1964, pp. 261-268. [cited by applicant]
He et al., “Guided Image Filtering,” European Conference on Computer Vision, 2010, 14 pages. [cited by applicant]
Heitz et al., “A Low-Discrepancy Sampler That Distributes Monte Carlo Errors as a Blue Noise in Screen Space,” ACM SIGGRAPH, 2019, 2 pages. [cited by applicant]
Heitz et al., “Combining Analytic Direct Illumination and Stochastic Shadows,” ACM Press, 2018, 10 pages. [cited by applicant]
Heitz et al., “Distributing Monte Carlo Errors as a Blue Noise in Screen Space by Permuting Pixel Seeds between Frames,” Eurographics Symposium on Rendering, 38(4): 2019, 10 pages. [cited by applicant]
Hey et al., “Importance Sampling with Hemispherical Particle Footprints,” Spring Conference on Computer Graphics, 2002, 11 pages. [cited by applicant]
IEEE, “IEEE Standard 754-2008 (Revision of IEEE Standard 754-1985): IEEE Standard for Floating-Point Arithmetic,” Aug. 29, 2008, 70 pages. [cited by applicant]
Jarosz et al., “A Comprehensive Theory of Volumetric Radiance Estimation Using Photon Points and Beams,” ACM Transactions on Graphics 30(1): Jan. 2011, 19 pages. [cited by applicant]
Jarosz et al., “Irradiance Gradients in the Presence of Participating Media and Occlusions,” Eurographics Symposium on Rendering, 27(4): Jun. 2008, 10 pages. [cited by applicant]
Jarosz et al., “Radiance Caching for Participating Media,” ACM Transactions on Graphics, 27(1): Mar. 2008, 29 pages. [cited by applicant]
Jarosz et al., “The Beam Radiance Estimate for Volumetric Photon Mapping,” Eurographics, 27(2): Apr. 2008, 10 pages. [cited by applicant]
Jarosz et al., Theory, Analysis and Applications of 2D Global Illumination. ACM Transactions on Graphics 31(5): Aug. 2012, 21 pages. [cited by applicant]
Jensen, “Global Illumination Using Photon Maps,” Eurographics Workshop on Rendering, 1996, 17 pages. [cited by applicant]
Jensen, “Importance Driven Path Tracing Using the Photon Map,” Eurographics Workshop on Rendering, 1995, 11 pages. [cited by applicant]
Jensen, “Realistic Image Synthesis Using Photon Mapping,” A K Peters Ltd, 2001, 183 pages. [cited by applicant]
Kalantari et al., “A Machine Learning Approach for Filtering Monte Carlo Noise, ” SIGGRAPH, 34(4): Jul. 2015, 12 pages. [cited by applicant]
Kelemen et al., “A Simple and Robust Mutation Strategy for the Metropolis Light Transport Algorithm,” Eurographics, 21(3): Sep. 2002, 11 pages. [cited by applicant]
Keller, “Instant Radiosity,” SIGGRAPH, ACM Press, 1997, 8 pages. [cited by applicant]
Kondapaneni et al., “Optimal Multiple Importance Sampling,” ACM Transactions on Graphics, 38(4): Jul. 2019, 14 pages. [cited by applicant]
Haines et al., “Ray Tracing Gem: High-Quality and Real-Time Rendering with DXR and Other APIs,” retrieved from https://link.springer.com/content/pdf/10.1007/978-1-4842-4427-2_18.pdf, Jan. 1, 2019, pp. 255-283. [cited by applicant]
Office Action for European Application No. 21769853.9, mailed Jun. 5, 2024, 8 pages. [cited by applicant]
Office Action for Chinese Application No. 202180004416.9, mailed Jan. 4, 2025, 26 pages. [cited by applicant]