Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Abstract

State-of-the-art techniques for 3D reconstruction are largely based on volumetric scene representations, which require sampling multiple points to compute the color arriving along a ray. Using these representations for more general inverse rendering—reconstructing geometry, materials, and lighting from observed images—is challenging because recursively path-tracing such volumetric representations is expensive. Recent works alleviate this issue through the use of radiance caches: data structures that store the steady-state, infinite-bounce radiance arriving at any point from any direction. However, these solutions rely on approximations that introduce bias into the renderings and, more importantly, into the gradients used for optimization. We present a method that avoids these approximations while remaining computationally efficient. In particular, we leverage two techniques to reduce variance for unbiased estimators of the rendering equation: (1) an occlusion-aware importance sampler for incoming illumination and (2) a fast cache architecture that can be used as a control variate for the radiance from a high-quality, but more expensive, volumetric cache. We show that by removing these biases our approach improves the generality of radiance cache based inverse rendering, as well as increasing quality in the presence of challenging light transport effects such as specular interreflections.

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

Access this chapter

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 64.99
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. Barron, J.T., Malik, J.: Intrinsic scene properties from a single RGB-D image. In: CVPR (2013)

    Google Scholar 

  2. Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-NeRF: anti-aliased grid-based neural radiance fields. In: ICCV (2023)

    Google Scholar 

  3. Bi, S., et al.: Neural reflectance fields for appearance acquisition (2020). arXiv:2008.03824

  4. 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. (2020)

    Google Scholar 

  5. Burley, B., Studios, W.D.A.: Physically-based shading at disney. ACM Trans. Graph. (2012)

    Google Scholar 

  6. Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: TensoRF: tensorial radiance fields. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 333–350. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19824-3_20

    Chapter  Google Scholar 

  7. Chen, W., et al.: Learning to predict 3D objects with an interpolation-based differentiable renderer. In: NeurIPS (2019)

    Google Scholar 

  8. Debevec, P.: Rendering synthetic objects into real scenes: bridging traditional and image-based graphics with global illumination and high dynamic range photography. ACM Trans. Graph. (1998)

    Google Scholar 

  9. Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. In: CVPR (2022)

    Google Scholar 

  10. Gkioulekas, I., Zhao, S., Bala, K., Zickler, T., Levin, A.: Inverse volume rendering with material dictionaries. ACM Trans. Graph. (2013)

    Google Scholar 

  11. Gupta, K., et al.: MCNeRF: Monte Carlo rendering and denoising for real-time NeRFs. ACM Trans. Graph. (2023)

    Google Scholar 

  12. Hasselgren, J., Hofmann, N., Munkberg, J.: Shape, light, and material decomposition from images using Monte Carlo rendering and denoising. In: NeurIPS (2022)

    Google Scholar 

  13. Hedman, P., Srinivasan, P.P., Mildenhall, B., Barron, J.T., Debevec, P.: Baking neural radiance fields for real-time view synthesis. In: ICCV (2021)

    Google Scholar 

  14. Jakob, W., Speierer, S., Roussel, N., Vicini, D.: Dr. JIT: a just-in-time compiler for differentiable rendering. ACM Trans. Graph. (2022)

    Google Scholar 

  15. Jin, H., et al.: TensoIR: tensorial inverse rendering. In: CVPR (2023)

    Google Scholar 

  16. Kajiya, J.T.: The rendering equation. ACM Trans. Graph. (1986)

    Google Scholar 

  17. Kalos, M.H., Whitlock, P.A.: Monte Carlo Methods. Wiley (2009)

    Google Scholar 

  18. Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. (2023)

    Google Scholar 

  19. Kloek, T., Van Dijk, H.K.: Bayesian estimates of equation system parameters: an application of integration by Monte Carlo. Econometrica J. Econometric Soc. (1978)

    Google Scholar 

  20. Krivánek, J., Gautron, P.: Practical Global Illumination with Irradiance Caching. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-79540-4

  21. Kuang, Z., et al.: Stanford-ORB: a real-world 3D object inverse rendering benchmark. In: NeurIPS Datasets & Benchmarks Track (2023)

    Google Scholar 

  22. Li, T.M., Aittala, M., Durand, F., Lehtinen, J.: Differentiable Monte Carlo ray tracing through edge sampling. ACM Trans. Graph. (2018)

    Google Scholar 

  23. Ling, J., Yu, R., Xu, F., Du, C., Zhao, S.: NeRF as non-distant environment emitter in physics-based inverse rendering. arXiv:2402.04829 (2024)

  24. Liu, I., et al.: OpenIllumination: a multi-illumination dataset for inverse rendering evaluation on real objects. In: NeurIPS (2024)

    Google Scholar 

  25. Liu, Y., et al.: NeRO: neural geometry and BRDF reconstruction of reflective objects from multiview images. ACM Trans. Graph. (2023)

    Google Scholar 

  26. Mai, A., Verbin, D., Kuester, F., Fridovich-Keil, S.: Neural microfacet fields for inverse rendering. In: ICCV (2023)

    Google Scholar 

  27. Max, N.: Optical models for direct volume rendering. IEEE Trans. Vis. Comput. Graph. (1995)

    Google Scholar 

  28. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NeRF: representing scenes as neural radiance fields for view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405–421. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_24

    Chapter  Google Scholar 

  29. Müller, T., McWilliams, B., Rousselle, F., Gross, M., Novák, J.: Neural importance sampling. ACM Trans. Graph. (2019)

    Google Scholar 

  30. Müller, T., Rousselle, F., Novák, J., Keller, A.: Real-time neural radiance caching for path tracing. ACM Trans. Graph. (2021)

    Google Scholar 

  31. Munkberg, J., et al.: Extracting triangular 3D models, materials, and lighting from images. In: CVPR (2022)

    Google Scholar 

  32. Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. (2022)

    Google Scholar 

  33. Nicolet, B., Rousselle, F., Novak, J., Keller, A., Jakob, W., Müller, T.: Recursive control variates for inverse rendering. ACM Trans. Graph. (2023)

    Google Scholar 

  34. Pharr, M., Jakob, W., Humphreys, G.: Physically Based Rendering: From Theory to Implementation. MIT Press (2023)

    Google Scholar 

  35. Ramamoorthi, R., Hanrahan, P.: A signal-processing framework for inverse rendering. ACM Trans. Graph. (2001)

    Google Scholar 

  36. Reiser, C., et al.: MERF: memory-efficient radiance fields for real-time view synthesis in unbounded scenes. ACM Trans. Graph. (2023)

    Google Scholar 

  37. Scherzer, D., Nguyen, C.H., Ritschel, T., Seidel, H.P.: Pre-convolved radiance caching. Comput. Graph. Forum (2012)

    Google Scholar 

  38. Silvennoinen, A., Lehtinen, J.: Real-time global illumination by precomputed local reconstruction from sparse radiance probes. ACM Trans. Graph. (TOG) (2017)

    Google Scholar 

  39. Srinivasan, P.P., Deng, B., Zhang, X., Tancik, M., Mildenhall, B., Barron, J.T.: NeRV: neural reflectance and visibility fields for relighting and view synthesis. In: CVPR (2021)

    Google Scholar 

  40. Veach, E.: Robust Monte Carlo Methods for Light Transport Simulation. Stanford University (1998)

    Google Scholar 

  41. Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: structured view-dependent appearance for neural radiance fields. In: CVPR (2022)

    Google Scholar 

  42. Ward, G.J., Rubinstein, F.M., Clear, R.D.: A ray tracing solution for diffuse interreflection. ACM Trans. Graph. (1988)

    Google Scholar 

  43. Yao, Y., et al.: NeiLF: neural incident light field for physically-based material estimation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13691, pp. 700–716. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-19821-2_40

    Chapter  Google Scholar 

  44. Yariv, L., et al.: BakedSDF: meshing neural SDFs for real-time view synthesis. In: SIGGRAPH (2023)

    Google Scholar 

  45. Yu, Y., Debevec, P., Malik, J., Hawkins, T.: Inverse global illumination: recovering reflectance models of real scenes from photographs. In: SIGGRAPH (1999)

    Google Scholar 

  46. Zhang, J., et al.: NeILF++: inter-reflectable light fields for geometry and material estimation. In: ICCV (2023)

    Google Scholar 

  47. Zhang, X., Srinivasan, P.P., Deng, B., Debevec, P., Freeman, W.T., Barron, J.T.: NeRFactor: neural factorization of shape and reflectance under an unknown illumination. ACM Trans. Graph. (2021)

    Google Scholar 

  48. Zhang, Y., Sun, J., He, X., Fu, H., Jia, R., Zhou, X.: Modeling indirect illumination for inverse rendering. In: CVPR (2022)

    Google Scholar 

Download references

Acknowledgements

This work was carried out while Benjamin was an intern at Google Research. Authors thank Rick Szeliski, Aleksander Holynski, and Janne Kontkanen for fruitful discussions. Benjamin Attal is supported by a Meta Research PhD Fellowship. Matthew O’Toole acknowledges support from NSF CAREER 2238485 and a Google gift.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benjamin Attal .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (zip 113445 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 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

Attal, B. et al. (2025). Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15086. Springer, Cham. https://doi.org/10.1007/978-3-031-73390-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-73390-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-73389-5

  • Online ISBN: 978-3-031-73390-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics