Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Enhanced Direct Lighting Using Visibility-Aware Light Sampling

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14496))

Included in the following conference series:

  • 786 Accesses

Abstract

Next event estimation has been widely applied to Monte Carlo rendering methods such as path tracing since estimating direct and indirect lighting separately often enables finding light paths from the eye to the lights effectively. Its success heavily relies on light sampling for direct lighting when a scene contains multiple light sources since each light can contribute differently to the reflected radiance on a surface point. We present a light sampling technique that can guide such a light selection to improve direct lighting. We estimate a spatially-varying function that approximates the contribution of each light on surface points within a discretized local area (i.e., a voxel in an adaptive octree) while considering the visibility between lights and surface points. We then construct a probability distribution function for sampling lights per voxel, which is proportional to our estimated function. We demonstrate that our light sampling technique can significantly improve rendering quality thanks to improved direct lighting with our light sampling.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Agarwal, S., Ramamoorthi, R., Belongie, S., Jensen, H.W.: Structured importance sampling of environment maps. ACM Trans. Graph. 22(3), 605–612 (2003)

    Article  Google Scholar 

  2. Bako, S., Meyer, M., DeRose, T., Sen, P.: Offline deep importance sampling for Monte Carlo path tracing. Comput. Graph. Forum 38(7), 527–542 (2019)

    Article  Google Scholar 

  3. Bitterli, B., Wyman, C., Pharr, M., Shirley, P., Lefohn, A., Jarosz, W.: Spatiotemporal reservoir resampling for real-time ray tracing with dynamic direct lighting. ACM Trans. Graph. 39(4), 1–17 (2020)

    Article  Google Scholar 

  4. Burke, D., Ghosh, A., Heidrich, W.: Bidirectional importance sampling for direct illumination. In: Proceedings of the 16th Eurographics Conference on Rendering Techniques, pp. 147–156 (2005)

    Google Scholar 

  5. Burley, B., et al.: The design and evolution of Disney’s Hyperion renderer. ACM Trans. Graph. 37(3), 1–22 (2018)

    Article  MathSciNet  Google Scholar 

  6. Clarberg, P., Akenine-Möllery, T.: Practical product importance sampling for direct illumination. Comput. Graph. Forum 27(2), 681–690 (2008)

    Article  Google Scholar 

  7. Conty Estevez, A., Kulla, C.: Importance sampling of many lights with adaptive tree splitting. Proc. ACM Comput. Graph. Interact. Tech. 1(2), 1–17 (2018)

    Article  Google Scholar 

  8. Diolatzis, S., Gruson, A., Jakob, W., Nowrouzezahrai, D., Drettakis, G.: Practical product path guiding using linearly transformed cosines. Comput. Graph. Forum 39(4), 23–33 (2020)

    Article  Google Scholar 

  9. Fascione, L., et al.: Manuka: a batch-shading architecture for spectral path tracing in movie production. ACM Trans. Graph. 37(3), 1–18 (2018)

    Article  Google Scholar 

  10. Georgiev, I., et al.: Arnold: a brute-force production path tracer. ACM Trans. Graph. 37(3), 1–12 (2018)

    Article  Google Scholar 

  11. Ghosh, A., Heidrich, W.: Correlated visibility sampling for direct illumination. Vis. Comput. 22(9), 693–701 (2006)

    Article  Google Scholar 

  12. Guo, J.J., Eisemann, M., Eisemann, E.: Next event estimation++: visibility mapping for efficient light transport simulation. Comput. Graph. Forum 39(7), 205–217 (2020)

    Article  Google Scholar 

  13. Herholz, S., Elek, O., Vorba, J., Lensch, H., Křivánek, J.: Product importance sampling for light transport path guiding. Comput. Graph. Forum 35(4), 67–77 (2016)

    Article  Google Scholar 

  14. Huo, Y., Wang, R., Zheng, R., Xu, H., Bao, H., Yoon, S.E.: Adaptive incident radiance field sampling and reconstruction using deep reinforcement learning. ACM Trans. Graph. 39(1), 1–17 (2020)

    Article  Google Scholar 

  15. Jakob, W.: Mitsuba renderer (2010). http://www.mitsuba-renderer.org

  16. Kajiya, J.T.: The rendering equation. SIGGRAPH Comput. Graph. 20(4), 143–150 (1986)

    Article  Google Scholar 

  17. Ki, H., Oh, K.: A GPU-based light hierarchy for real-time approximate illumination. Vis. Comput. 24(7), 649–658 (2008)

    Article  Google Scholar 

  18. Lai, Y.C., Chou, H.T., Chen, K.W., Fan, S.: Robust and efficient adaptive direct lighting estimation. Vis. Comput. 31(1), 83–91 (2015)

    Article  Google Scholar 

  19. Lawrence, J., Rusinkiewicz, S., Ramamoorthi, R.: Efficient BRDF importance sampling using a factored representation. ACM Trans. Graph. 23(3), 496–505 (2004)

    Article  Google Scholar 

  20. Liu, Y., Xu, K., Yan, L.Q.: Adaptive BRDF-oriented multiple importance sampling of many lights. Comput. Graph. Forum 38(4), 123–133 (2019)

    Article  Google Scholar 

  21. Müller, T., Gross, M., Novák, J.: Practical path guiding for efficient light-transport simulation. Comput. Graph. Forum 36(4), 91–100 (2017)

    Article  Google Scholar 

  22. Müller, T., Mcwilliams, B., Rousselle, F., Gross, M., Novák, J.: Neural importance sampling. ACM Trans. Graph. 38(5), 1–19 (2019)

    Article  Google Scholar 

  23. Ostromoukhov, V., Donohue, C., Jodoin, P.M.: Fast hierarchical importance sampling with blue noise properties. ACM Trans. Graph. 23(3), 488–495 (2004)

    Article  Google Scholar 

  24. Pantaleoni, J.: Importance sampling of many lights with reinforcement lightcuts learning. arXiv preprint arXiv:1911.10217 (2019)

  25. Pharr, M., Jakob, W., Humphreys, G.: Physically Based Rendering: From Theory to Implementation, 3rd edn. Morgan Kaufmann Publishers Inc., Burlington (2016)

    Google Scholar 

  26. Rath, A., Grittmann, P., Herholz, S., Vévoda, P., Slusallek, P., Křivánek, J.: Variance-aware path guiding. ACM Trans. Graph. 39(4), 1–12 (2020)

    Article  Google Scholar 

  27. Rousselle, F., Knaus, C., Zwicker, M.: Adaptive sampling and reconstruction using greedy error minimization. ACM Trans. Graph. 30(6), 1–12 (2011)

    Article  Google Scholar 

  28. Sbert, M., Havran, V.: Adaptive multiple importance sampling for general functions. Vis. Comput. 33(6), 845–855 (2017)

    Article  Google Scholar 

  29. Shirley, P., Wang, C., Zimmerman, K.: Monte Carlo techniques for direct lighting calculations. ACM Trans. Graph. 15(1), 1–36 (1996)

    Article  Google Scholar 

  30. Veach, E.: Robust Monte Carlo methods for light transport simulation. Stanford University (1998)

    Google Scholar 

  31. Veach, E., Guibas, L.: Bidirectional estimators for light transport. In: Sakas, G., Müller, S., Shirley, P. (eds.) Photorealistic Rendering Techniques, pp. 145–167. Springer, Heidelberg (1995). https://doi.org/10.1007/978-3-642-87825-1_11

    Chapter  Google Scholar 

  32. Veach, E., Guibas, L.J.: Metropolis light transport. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1997, pp. 65–76. ACM Press/Addison-Wesley Publishing Co. (1997)

    Google Scholar 

  33. Vévoda, P., Kondapaneni, I., Křivánek, J.: Bayesian online regression for adaptive direct illumination sampling. ACM Trans. Graph. 37(4), 1–12 (2018)

    Article  Google Scholar 

  34. Vorba, J., Karlík, O., Šik, M., Ritschel, T., Křivánek, J.: On-line learning of parametric mixture models for light transport simulation. ACM Trans. Graph. 33(4), 1–11 (2014)

    Article  Google Scholar 

  35. Walter, B., Fernandez, S., Arbree, A., Bala, K., Donikian, M., Greenberg, D.P.: Lightcuts: a scalable approach to illumination. ACM Trans. Graph. 24(3), 1098–1107 (2005)

    Article  Google Scholar 

  36. Wang, Y.C., Wu, Y.T., Li, T.M., Chuang, Y.Y.: Learning to cluster for rendering with many lights. ACM Trans. Graph. 40(6), 1–10 (2021)

    Google Scholar 

  37. Zheng, Q., Zwicker, M.: Learning to importance sample in primary sample space. Comput. Graph. Forum 38(2), 169–179 (2019)

    Article  Google Scholar 

  38. Zhu, S., et al.: Hierarchical neural reconstruction for path guiding using hybrid path and photon samples. ACM Trans. Graph. 40(4), 1–16 (2021)

    Google Scholar 

  39. Zhu, S., et al.: Photon-driven neural reconstruction for path guiding. ACM Trans. Graph. 41(1), 1–15 (2021)

    Article  Google Scholar 

Download references

Acknowledgements

We thank the anonymous reviewers for feedback on our paper and the following authors and artists for the 3D models or scenes: Asian Dragon (the Stanford Computer Graphics Laboratory), Lamp (UP3D), Whiteroom (Jay-Artist), and Veach-Ajar (Benedikt Bitterli). The Hotel scene was bought from TurboSquid. This work was supported in part by Ministry of Culture, Sports and Tourism and Korea Creative Content Agency (No. R2021080001) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00207939).

Author information

Authors and Affiliations

  1. Gwangju Institute of Science and Technology, Gwangju, 61005, South Korea

    Geonu Noh, Hajin Choi & Bochang Moon

Authors
  1. Geonu Noh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hajin Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bochang Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bochang Moon .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  2. Shanghai Jiao Tong University, Shanghai, China

    Lei Bi

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. MIRALab-CUI, University of Geneve, Carouge, Geneve, Switzerland

    Nadia Magnenat-Thalmann

  5. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Noh, G., Choi, H., Moon, B. (2024). Enhanced Direct Lighting Using Visibility-Aware Light Sampling. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14496. Springer, Cham. https://doi.org/10.1007/978-3-031-50072-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50072-5_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50071-8

  • Online ISBN: 978-3-031-50072-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation