skip to main content
research-article
Open Access

Efficient estimation of boundary integrals for path-space differentiable rendering

Published:22 July 2022Publication History
Skip Abstract Section

Abstract

Boundary integrals are unique to physics-based differentiable rendering and crucial for differentiating with respect to object geometry. Under the differential path integral framework---which has enabled the development of sophisticated differentiable rendering algorithms---the boundary components are themselves path integrals. Previously, although the mathematical formulation of boundary path integrals have been established, efficient estimation of these integrals remains challenging.

In this paper, we introduce a new technique to efficiently estimate boundary path integrals. A key component of our technique is a primary-sample-space guiding step for importance sampling of boundary segments. Additionally, we show multiple importance sampling can be used to combine multiple guided samplings. Lastly, we introduce an optional edge sorting step to further improve the runtime performance. We evaluate the effectiveness of our method using several differentiable-rendering and inverse-rendering examples and provide comparisons with existing methods for reconstruction as well as gradient quality.

Skip Supplemental Material Section

Supplemental Material

3528223.3530080.mp4

presentation

References

  1. Michael Ashikhmin and Peter Shirley. 2000. An anisotropic phong BRDF model. Journal of graphics tools 5, 2 (2000), 25--32.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Sai Praveen Bangaru, Tzu-Mao Li, and Frédo Durand. 2020. Unbiased Warped-Area Sampling for Differentiable Rendering. ACM Trans. Graph. 39, 6 (2020), 245:1--245:18.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Harry G Barrow, Jay M Tenenbaum, Robert C Bolles, and Helen C Wolf. 1977. Parametric correspondence and chamfer matching: Two new techniques for image matching. Technical Report. SRI INTERNATIONAL MENLO PARK CA ARTIFICIAL INTELLIGENCE CENTER.Google ScholarGoogle Scholar
  4. Robert L Cook and Kenneth E. Torrance. 1982. A reflectance model for computer graphics. ACM Transactions on Graphics (ToG) 1, 1 (1982), 7--24.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Yue Dong, Guojun Chen, Pieter Peers, Jiawan Zhang, and Xin Tong. 2014. Appearance-from-motion: Recovering spatially varying surface reflectance under unknown lighting. ACM Trans. Graph. 33, 6 (2014), 1--12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Jerry Guo, Pablo Bauszat, Jacco Bikker, and Elmar Eisemann. 2018. Primary sample space path guiding. In Eurographics Symposium on Rendering, Vol. 2018. 73--82.Google ScholarGoogle Scholar
  7. Eric Heitz and Eugene d'Eon. 2014. Importance sampling microfacet-based BSDFs using the distribution of visible normals. In Computer Graphics Forum, Vol. 33. Wiley Online Library, 103--112.Google ScholarGoogle Scholar
  8. Eric Heitz, Johannes Hanika, Eugene d'Eon, and Carsten Dachsbacher. 2016. Multiple-scattering microfacet BSDFs with the Smith model. ACM Trans. Graph. 35, 4 (2016), 58:1--58:14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Wenzel Jakob, Marco Tarini, Daniele Panozzo, and Olga Sorkine-Hornung. 2015. Instant Field-Aligned Meshes. ACM Trans. Graph. 34, 6 (2015), 189:1--189:15.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Henrik Wann Jensen. 1995. Importance driven path tracing using the photon map. In Eurographics Workshop on Rendering Techniques. Springer, 326--335.Google ScholarGoogle ScholarCross RefCross Ref
  11. Rasmus Jensen, Anders Dahl, George Vogiatzis, Engin Tola, and Henrik Aanæs. 2014. Large scale multi-view stereopsis evaluation. In CVPR. 406--413.Google ScholarGoogle Scholar
  12. Csaba Kelemen and Laszlo Szirmay-Kalos. 2001. A microfacet based coupled specular-matte BRDF model with importance sampling. In Eurographics short presentations, Vol. 2. 4.Google ScholarGoogle Scholar
  13. Eric P Lafortune and Yves D Willems. 1995. A 5D tree to reduce the variance of Monte Carlo ray tracing. In Eurographics Workshop on Rendering Techniques. Springer, 11--20.Google ScholarGoogle ScholarCross RefCross Ref
  14. Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. 2020. Modular Primitives for High-Performance Differentiable Rendering. ACM Trans. Graph. 39, 6 (2020), 194:1--194:14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Joo Ho Lee, Adrian Jarabo, Daniel S. Jeon, Diego Gutierrez, and Min H. Kim. 2018. Practical multiple scattering for rough surfaces. ACM Trans. Graph. 37, 6 (2018), 275:1--275:12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen. 2018. Differentiable Monte Carlo ray tracing through edge sampling. ACM Trans. Graph. 37, 6 (2018), 222:1--222:11.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Guillaume Loubet, Nicolas Holzschuch, and Wenzel Jakob. 2019. Reparameterizing discontinuous integrands for differentiable rendering. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1--14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Thomas Müller, Markus Gross, and Jan Novák. 2017. Practical path guiding for efficient light-transport simulation. In Computer Graphics Forum, Vol. 36. 91--100.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Thomas Müller, Brian Mcwilliams, Fabrice Rousselle, Markus Gross, and Jan Novák. 2019. Neural Importance Sampling. ACM Trans. Graph. 38, 5 (2019), 145:1--145:19.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Baptiste Nicolet, Alec Jacobson, and Wenzel Jakob. 2021. Large Steps in Inverse Rendering of Geometry. ACM Trans. Graph. 40, 6 (2021), 248:1--248:13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Merlin Nimier-David, Delio Vicini, Tizian Zeltner, and Wenzel Jakob. 2019. Mitsuba 2: a retargetable forward and inverse renderer. ACM Transactions on Graphics (TOG) 38, 6 (2019), 203.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Michael Oren and Shree K Nayar. 1994. Generalization of Lambert's reflectance model. In Proceedings of the 21st annual conference on Computer graphics and interactive techniques. 239--246.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Bui Tuong Phong. 1975. Illumination for computer generated pictures. Commun. ACM 18, 6 (1975), 311--317.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sylvia C Pont and Jan J Koenderink. 2002. Bidirectional reflectance distribution function of specular surfaces with hemispherical pits. JOSA A 19, 12 (2002), 2456--2466.Google ScholarGoogle ScholarCross RefCross Ref
  25. Christophe Schlick. 1994. An inexpensive BRDF model for physically-based rendering. In Computer graphics forum, Vol. 13. Wiley Online Library, 233--246.Google ScholarGoogle Scholar
  26. Bram van Ginneken, Marigo Stavridi, and Jan J Koenderink. 1998. Diffuse and specular reflectance from rough surfaces. Applied optics 37, 1 (1998), 130--139.Google ScholarGoogle Scholar
  27. E. Veach. 1997. Robust Monte Carlo methods for light transport simulation. Vol. 1610. Stanford University PhD thesis.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Jiří Vorba, Ondřej Karlík, Martin Šik, Tobias Ritschel, and Jaroslav Křivánek. 2014. On-Line Learning of Parametric Mixture Models for Light Transport Simulation. ACM Trans. Graph. 33, 4 (2014), 101:1--101:11.Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Bruce Walter, Stephen R Marschner, Hongsong Li, and Kenneth E Torrance. 2007. Microfacet models for refraction through rough surfaces. Rendering techniques 2007 (2007), 18th.Google ScholarGoogle Scholar
  30. Gregory J Ward. 1992. Measuring and modeling anisotropic reflection. In Proceedings of the 19th annual conference on Computer graphics and interactive techniques. 265--272.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Feng Xie and Pat Hanrahan. 2018. Multiple scattering from distributions of specular V-grooves. ACM Trans. Graph. 37, 6 (2018), 276:1--2767:14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Tizian Zeltner, Sébastien Speierer, Iliyan Georgiev, and Wenzel Jakob. 2021. Monte Carlo Estimators for Differential Light Transport. ACM Trans. Graph. 40, 4 (2021), 78:1--78:16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Cheng Zhang, Zhao Dong, Michael Doggett, and Shuang Zhao. 2021a. Antithetic Sampling for Monte Carlo Differentiable Rendering. ACM Trans. Graph. 40, 4 (2021), 77:1--77:12.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. C. Zhang, B. Miller, K. Yan, I. Gkioulekas, and S. Zhao. 2020. Path-Space Differentiable Rendering. ACM Transactions on Graphics (SIGGRAPH 2020) 39, 4 (2020), 143:1--143:19.Google ScholarGoogle Scholar
  35. Cheng Zhang, Lifan Wu, Changxi Zheng, Ioannis Gkioulekas, Ravi Ramamoorthi, and Shaung Zhao. 2019. A differential theory of radiative transfer. ACM Trans. Graph. 38, 6 (2019), 227:1--227:16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Cheng Zhang, Zihan Yu, and Shuang Zhao. 2021b. Path-Space Differentiable Rendering of Participating Media. ACM Trans. Graph. 40, 4 (2021), 76:1--76:15.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Quan Zheng and Matthias Zwicker. 2019. Learning to importance sample in primary sample space. In Computer Graphics Forum, Vol. 38. 169--179.Google ScholarGoogle ScholarCross RefCross Ref
  38. Yang Zhou, Lifan Wu, Ravi Ramamoorthi, and Ling-Qi Yan. 2021. Vectorization for Fast, Analytic, and Differentiable Visibility. ACM Trans. Graph. 40, 3 (2021), 27:1--27:21.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Efficient estimation of boundary integrals for path-space differentiable rendering

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in

    Full Access

    • Published in

      cover image ACM Transactions on Graphics
      ACM Transactions on Graphics  Volume 41, Issue 4
      July 2022
      1978 pages
      ISSN:0730-0301
      EISSN:1557-7368
      DOI:10.1145/3528223
      Issue’s Table of Contents

      Copyright © 2022 Owner/Author

      This work is licensed under a Creative Commons Attribution International 4.0 License.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 July 2022
      Published in tog Volume 41, Issue 4

      Check for updates

      Qualifiers

      • research-article

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader