Abstract
Restricting path tracing to a small number of paths per pixel in order to render images faster rarely achieves a satisfactory image quality for scenes of interest. However, path space filtering may dramatically improve the visual quality by sharing information across vertices of paths classified as proximate. Unlike screen space approaches, these paths neither need to be present on the screen, nor is filtering restricted to the first intersection with the scene. While searching proximate vertices had been more expensive than filtering in screen space, we greatly improve over this performance penalty by storing, updating, and looking up the required information in a hash table. The keys are constructed from jittered and quantized information, such that only a single query very likely replaces costly neighborhood searches. A massively parallel implementation of the algorithm is demonstrated on a graphics processing unit (GPU).
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References
Bako, S., Vogels, T., McWilliams, B., Meyer, M., Novák, J., Harvill, A., Sen, P., Derose, T., Rousselle, F.: Kernel-predicting convolutional networks for denoising Monte Carlo renderings. ACM Trans. Graph. 36(4), 97:1–97:14 (2017). https://doi.org/10.1145/3072959.3073708
Bekaert, P., Sbert, M., Halton, J.: Accelerating path tracing by re-using paths. In: Debevec, P., Gibson, S. (eds.) Eurographics Workshop on Rendering. The Eurographics Association (2002). https://doi.org/10.2312/EGWR/EGWR02/125-134
Boissé, G.: World-space spatiotemporal reservoir reuse for ray-traced global illumination. ACM Trans. Graph. 40(6) (2021)
Chaitanya, C., Kaplanyan, A., Schied, C., Salvi, M., Lefohn, A., Nowrouzezahrai, D., Aila, T.: Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder. ACM Trans. Graph. 36(4), 98:1–98:12 (2017). https://doi.org/10.1145/3072959.3073601
Cigolle, Z., Donow, S., Evangelakos, D., Mara, M., McGuire, M., Meyer, Q.: A survey of efficient representations for independent unit vectors. J. Comput. Graph. Tech. (JCGT) 3(2), 1–30 (2014). http://jcgt.org/published/0003/02/01/
Dahm, K., Keller, A.: Learning light transport the reinforced way. In: Owen, A., Glynn, P. (eds.) Monte Carlo and Quasi-Monte Carlo Methods. MCQMC 2016. Proceedings in Mathematics & Statistics, vol. 241, pp. 181–195. Springer, Berlin (2018)
Dammertz, H.: Acceleration methods for ray tracing based global illumination. Ph.D. thesis, Universität Ulm (2011)
Dietrich, A., Slusallek, P.: Adaptive spatial sample caching. In: 2007 IEEE Symposium on Interactive Ray Tracing, pp. 141–147 (2007). https://doi.org/10.1109/RT.2007.4342602
Gautron, P.: Real-time ray-traced ambient occlusion of complex scenes using spatial hashing. In: Special Interest Group on Computer Graphics and Interactive Techniques Conference Talks, SIGGRAPH ’20. Association for Computing Machinery, New York, USA (2020)
Gautron, P.: Practical spatial hash map updates. In: Ray Tracing Gems II: Next Generation Real-Time Rendering with DXR. Vulkan, and OptiX, pp. 659–671. Apress, Berkeley, CA (2021)
Gautron, P., Droske, M., Wächter, C., Kettner, L., Keller, A., Binder, N., Dahm, K.: Path space similarity determined by Fourier histogram descriptors. In: ACM SIGGRAPH 2014 Talks, SIGGRAPH ’14, pp. 39:1–39:1. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2614106.2614117
Georgiev, I., Křivánek, J., Davidovič, T., Slusallek, P.: Light transport simulation with vertex connection and merging. ACM Trans. Graph. 31(6), 192:1–192:10 (2012)
Hachisuka, T., Jensen, H.: Stochastic progressive photon mapping. In: SIGGRAPH Asia ’09: ACM SIGGRAPH Asia 2009 papers, pp. 1–8. ACM (2009)
Hachisuka, T., Jensen, H.: Parallel progressive photon mapping on GPUs. SIGGRAPH Sketches (2010). https://doi.org/10.1145/1899950.1900004
Hachisuka, T., Pantaleoni, J., Jensen, H.W.: A path space extension for robust light transport simulation. ACM Trans. Graph. 31(6) (2012). https://doi.org/10.1145/2366145.2366210
Havran, V., Herzog, R., Seidel, H.P.: Fast final gathering via reverse photon mapping. Comput. Graph. Forum 24(3), 323–332 (2005)
Jensen, H.: Realistic Image Synthesis Using Photon Mapping. AK Peters (2001)
Keller, A.: Quasi-Monte Carlo Methods for Photorealistic Image Synthesis. Ph.D. thesis, University of Kaiserslautern, Germany (1998)
Keller, A., Dahm, K., Binder, N.: Path space filtering. In: Cools, R., Nuyens, D. (eds.) Monte Carlo and Quasi-Monte Carlo Methods 2014, pp. 423–436. Springer, Berlin (2016)
Kontkanen, J., Räsänen, J., Keller, A.: Irradiance filtering for Monte Carlo ray tracing. In: Talay, D., Niederreiter, H. (eds.) Monte Carlo and Quasi-Monte Carlo Methods 2004, pp. 259–272. Springer, Berlin (2004)
Lafortune, E., Willems, Y.: Bi-directional path tracing. In: Proceedings of Third International Conference on Computational Graphics and Visualization Techniques (Compugraphics’ 93) (1998)
Ma, V., McCool, M.: Low latency photon mapping using block hashing. In: Ertl, T., Heidrich, W., Doggett, M. (eds.) SIGGRAPH/Eurographics Workshop on Graphics Hardware. The Eurographics Association (2002). https://doi.org/10.2312/EGGH/EGGH02/089-098
Mara, M., Luebke, D., McGuire, M.: Toward practical real-time photon mapping: efficient GPU density estimation. In: Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (I3D’13) (2013). https://casual-effects.com/research/Mara2013Photon/index.html
Mara, M., McGuire, M., Bitterli, B., Jarosz, W.: An efficient denoising algorithm for global illumination. In: ACM SIGGRAPH/Eurographics High Performance Graphics, p. 7 (2017). http://casual-effects.com/research/Mara2017Denoise/index.html
McCool, M.: Anisotropic diffusion for Monte Carlo noise reduction. ACM Trans. Graph. 18 (2002). https://doi.org/10.1145/318009.318015
Müller, R., McWilliams, B., Rousselle, F., Gross, M., Novák, J.: Neural importance sampling. ACM Trans. Graph. 38(5), 145:1–145:19 (2019)
Müller, T., Gross, M., Novák, J.: Practical path guiding for efficient light-transport simulation. Comput. Graph. Forum 36(4), 91–100 (2017)
Munkberg, J., Hasselgren, J., Clarberg, P., Andersson, M., Akenine-Möller, T.: Texture space caching and reconstruction for ray tracing. ACM Trans. Graph. 35(6), 249:1–249:13 (2016). https://doi.org/10.1145/2980179.2982407
Pantaleoni, J.: Online path sampling control with progressive spatio-temporal filtering (2020)
Rabin, M.: Fingerprinting By Random Polynomials. Center for Research in Computing Technology, Harvard University, Technical report (1981)
Schied, C., Kaplanyan, A., Wyman, C., Patney, A., Chaitanya, C., Burgess, J., Liu, S., Dachsbacher, C., Lefohn, A., Salvi, M.: Spatiotemporal variance-guided filtering: Real-time reconstruction for path-traced global illumination. In: Proceedings of High Performance Graphics, HPG ’17, pp. 2:1–2:12. ACM, New York, NY, USA (2017). https://doi.org/10.1145/3105762.3105770
Schied, C., Peters, C., Dachsbacher, C.: Gradient estimation for real-time adaptive temporal filtering. Proc. ACM Comput. Graph. Interact. Tech. 1(2) (2018)
Sen, P., Zwicker, M., Rousselle, F., Yoon, S.E., Kalantari, N.: Denoising your Monte Carlo renders: recent advances in image-space adaptive sampling and reconstruction. In: ACM SIGGRAPH 2015 Courses, SIGGRAPH ’15, pp. 11:1–11:255. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2776880.2792740
Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors [lecture notes]. IEEE Signal Process. Mag. 25(2), 128–131 (2008). https://doi.org/10.1109/MSP.2007.914237
Veach, E.: Robust Monte Carlo Methods for Light Transport Simulation. Ph.D. thesis, Stanford University (1997)
West, R., Georgiev, I., Gruson, A., Hachisuka, T.: Continuous multiple importance sampling. ACM Trans. Graph. (Proceedings of SIGGRAPH) 39(4) (2020)
Zwicker, M., Jarosz, W., Lehtinen, J., Moon, B., Ramamoorthi, R., Rousselle, F., Sen, P., Soler, C., Yoon, S.E.: Recent advances in adaptive sampling and reconstruction for Monte Carlo rendering. Comput. Graph. Forum 34(2), 667–681 (2015)
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The authors thank Petrik Clarberg for profound discussions and comments.
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Binder, N., Fricke, S., Keller, A. (2022). Massively Parallel Path Space Filtering. In: Keller, A. (eds) Monte Carlo and Quasi-Monte Carlo Methods. MCQMC 2020. Springer Proceedings in Mathematics & Statistics, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-030-98319-2_7
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