Interactive calculation of light refraction and caustics using a graphics processor

Мұқаба

Дәйексөз келтіру

Толық мәтін

Ашық рұқсат Ашық рұқсат
Рұқсат жабық Рұқсат берілді
Рұқсат жабық Тек жазылушылар үшін

Аннотация

While modern rendering systems are effective at modeling complex light paths in complex environments, rendering refractive caustics still takes a long time. Caustics are light patterns that occur when light is refracted and reflected from a surface. Due to the sharp density distribution of these mirror events, rendering algorithms mainly rely on direct sampling of the bidirectional scattering distribution function on these surfaces to plot trajectories. This requires many calculations. Photonic maps are also used. However, there are problems limiting the applicability of caustic maps. Since each photon in the photon buffer must be processed, therefore, one has to choose between a strongly underestimated caustic sampling and a large decrease in speed in order to use a sufficient number of photons for caustics in order to obtain high-quality images. Complex mirror interactions cause oversampling in bright focal areas, while other areas of the caustic map remain under-selected and noisy. At the same time, speed takes precedence over realism in most interactive applications. However, the desire to improve the quality of graphics prompted the development of various fast approximations for realistic lighting.

This paper presents a combined method for visualizing refraction of light and caustics using reverse integration for illumination and direct integration for viewing rays. An approach is used for simultaneous propagation of light and for tracking rays in volume and, therefore, it does not require storing data of an intermediate volume of illumination. In the implementation of the method, the distance between the light planes is set to one voxel, which provides at least one sample per voxel for all orientations. The method does not use preliminary calculations; all rendering parameters can be changed interactively.

As a result, using the proposed method, it is possible to create plausible approximations of complex phenomena such as refractions and caustics. The effect of refraction on the shadow is shown. Complex light patterns are demonstrated due to the curved geometry of the objects. The visualization results show the importance of refraction for the appearance of transparent objects. For example, distortions caused by refraction and refraction at the interface between media. The difference in refractive indices between individual media causes a complex interaction between light and shadow areas. It is shown how refraction and caustics improve the visualization of functionally defined objects by providing additional information about shape and location.

Толық мәтін

Рұқсат жабық

Авторлар туралы

S. Vyatkin

Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences

Хат алмасуға жауапты Автор.
Email: sivser@mail.ru
ORCID iD: 0000-0002-1591-3588

Synthesizing Visualization Systems Laboratory

Ресей, Academician Koptyug Avenue, 1, Novosibirsk, 630090

B. Dolgovesov

Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences

Email: bsd@iae.nsk.su
ORCID iD: 0000-0002-6255-9315

Synthesizing Visualization Systems Laboratory

Ресей, Academician Koptyug Avenue, 1, Novosibirsk, 630090

Әдебиет тізімі

  1. Wang X., Zhang R. Rendering transparent objects with caustics using real-time ray tracing, Comput. & Graph., 2021, vol. 96, no. 3. doi: 10.1016/j.cag.2021.03.003.
  2. Komarov E., Zhdanov D., Zhdanov A. Rendering the real-time caustics with DirectX raytracing, 31th Int. Conference on Computer Graphics and Vision (Graphicon-2021), pp. 36–47. doi: 10.20948/graphicon-2021-3027-36-47.
  3. Grittmann P., Pérard-Gayot A., Slusallek P., Krivanek J. Efficient caustic rendering with lightweight photon mapping, Comput. Graph. Forum, 2018, vol. 37, no. 4, pp. 133–142. doi: 10.1111/cgf.13481.
  4. Muller T., Gross M., Novak J. Practical path guiding for efficient light-transport simulation, Comput. Graph. Forum, 2017, vol. 36, no. 4, pp. 91–100. doi: 10.1111/cgf.13227.
  5. Rodriguez S., Leimkuhler T., Prakash S., Wyman C., Shirley P., Drettakis G. Glossy probe reprojection for interactive global illumination, ACM Trans. Graph., 2020, vol. 39, no. 6, pp. 1–16. doi: 10.1145/3414685.3417823.
  6. Kopanas G., Leimkuhler T., Rainer G., Jambon C. Drettakis G. Neural point catacaustics for novel-view synthesis of reflections, ACM Trans. Graph., 2022, vol. 41, no. 6, pp. 1–15. doi: 10.1145/3550454.3555497.
  7. Vyatkin S.I., Dolgovesov B.S. Highly Realistic Visualization of Caustics and Rough Surfaces, Program. Comput. Software, 2022, vol. 48, no. 5, pp. 322–330. doi: 10.31857/S0132347422050065.
  8. Haber J., Magnor M., Seidel H.P. Physically-based Simulation of Twilight Phenomena, ACM Trans. Graph., vol. 24, no. 4, 2005, pp. 1353–1373. doi: 10.1145/1095878.1095884.
  9. Vyatkin S.I., Dolgovesov B.S. Physically based rendering of functionally defined objects, Optoelectron., Instrum. Data Process., 2022, vol. 58, no. 3, pp. 291–297. doi: 10.15372/AUT20220311.
  10. Vyatkin S.I., Dolgovesov B.S. A Method for visualizing multivolume data and functionally defined surfaces using GPUs, Optoelectron., Instrum. Data Process., 2021, vol. 57, no. 2, pp. 32–40. doi: 10.15372/AUT20210204.
  11. Vyatkin S.I. Method of binary search for image elements of functionally defined objects using graphics processing units, Optoelectron., Instrum. Data Process., 2914, vol. 50, no. 6, pp. 606–612.
  12. Galtier M., Blanco S., Caliot C., et al. Integral formulation of null collision Monte Carlo algorithms, J. Quantitative Spectrosc. Radiative Transfer., 2013, vol. 125, pp. 57–68. doi: 10.1016/j.jqsrt.2013.04.001.
  13. Jensen H.W., Marschner S.R., Levoy M. A practical model for subsurface light transport, Proc. of the 28th annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH ‘01, New York: ACM, 2001, pp. 511–518. doi: 10.1145/383259.383319.
  14. Loube G., Zeltner T., Holzschuch N. Slope-space integrals for specular next event estimation, ACM Trans. Graph., 2020, vol. 39, no. 6. pp. 1–13. doi: 10.1145/3414685.3417811.
  15. Deng X., Jiao S., Bitterli B., Jarosz W. Photon surfaces for robust, unbiased volumetric density estimation”, ACM Trans. Graph., 2019, vol. 38, no. 4, pp. 1–12. doi: 10.1145/3306346.3323041.
  16. Bitterli B., Jarosz W. Beyond points and beams: Higher dimensional photon samples for volumetric light transport, ACM Trans. Graph., 2017, vol. 36, no. 4, pp. 1–12.
  17. Frolov V.A., Voloboy A.G., Ershov S.V., Galaktionov V.A. State-of-the art of methods for global illumination calculation in realistic computer graphics, Trudy ISP RAN, 2021, vol. 33, no. 2, pp. 7–48. doi:https://doi.org/10.15514/ISPRAS-2021-33(2)-1.
  18. Vyatkin S.I., Dolgovesov B.S. Functionally defined models for additive production, Issled. Innovatsii, Praktika, 2022, vol. 4, no. 4, pp. 16–25. doi: 10.18411/iip -08-2022-04.
  19. Lavoue G., Bonneel N., Farrugia J.-P., Soler C. Perceptual quality of BRDF approximations: Dataset and metrics, Comp. Graph. Forum, 2021, vol. 40, no. 2, pp. 327–338. doi: 10.1111/cgf.142636.
  20. Vyatkin S.I., Dolgovesov B.S. Smoothing functionally specified objects in scenes with global illumination, J. Adv. Res. Techn. Sci., 2022, no. 30, pp. 96–103. doi: 10.26160/2474-5901-2022-30-96-103.
  21. Schlick C. A Customizable reflectance model for everyday rendering, Proc. of the Fourth Eurographics Workshop on Rendering, 1993, pp. 73–84. Corpus ID: 18967314.
  22. Jensen H.W. Realistic Image Synthesis Using Photon Mapping, Natick, Mass.: Peters, 2001.
  23. Kang C-M., Wang L., Xu Y., Meng X. A survey of photon mapping state-of-the-art research and future challenges, Frontiers Inf. Technol. & Electron. Eng., 2016, vol. 17, no. 3, pp. 185–199. doi: 10.1631/FITEE.1500251.
  24. Fabianowski B., Dingliana J. Interactive global photon mapping, Comput. Graph. Forum, 2009, vol. 28, no. 4, pp. 1151–1159. http://dx.doi.org/10.1111/j.1467-8659.2009.01492.x.
  25. Pediredla A., Chalmiani Y.K., Scopelliti M.G., Chamanzar M., Narasimhan S. Path tracing estimators for refractive radiative transfer, ACM Trans. Graph., 2020, vol. 39, no. 6, pp. 1–15. doi: 10.1145/3414685.3417793.
  26. Davidovic T., Krivanek J., Hasan M., Slusallek P. Progressive light transport simulation on the GPU: Survey and improvements, ACM Trans. Graph., 2014, vol. 33, no. 3, pp. 1–19. http://dx.doi.org/10.1145/2602144.

Қосымша файлдар

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Әрекет
1. JATS XML
2. Fig. 1. Subsurface light scattering.

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3. Fig. 2. The contribution of planes to the volume.

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4. Fig. 3. The visualization algorithm.

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5. Fig. 4. On the left: a transparent ellipsoid with caustics. On the right: the same object visualized using the method [7].

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6. Fig. 5. On the left: a transparent object with caustics. On the right: the same object visualized using the method [7].

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7. Fig. 6. On the left: a transparent vessel with liquid and caustic. On the right: the same object visualized using the method [7].

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8. Fig. 7. Additional calculation time.

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© Russian Academy of Sciences, 2024

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