mlsanityData to 7 Oct 2026

Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering

arXiv 2409.05867 ↗First posted 9 Sep 2024cs.CV cs.GRcited by 12

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Abstract (arXiv)

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 reflections.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 26 of its 49 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

26 of 49 references.

References · 26

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  3. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  4. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  5. 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 667
  6. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
  7. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 493
  8. 2021Baking Neural Radiance Fields for Real-Time View Synthesiscited by 465
  9. 2021Extracting Triangular 3D Models, Materials, and Lighting From Imagescited by 363
  10. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336
  11. 2019Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderercited by 221
  12. 2023BakedSDF: Meshing Neural SDFs for Real-Time View Synthesiscited by 220
14 more
  1. 2023MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenescited by 215
  2. 2020Neural Reflectance Fields for Appearance Acquisitioncited by 196
  3. 2022Modeling Indirect Illumination for Inverse Renderingcited by 167
  4. 2023TensoIR: Tensorial Inverse Renderingcited by 166
  5. 2022Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoisingcited by 163
  6. 2023NeRO: Neural Geometry and BRDF Reconstruction of Reflective Objects from Multiview Imagescited by 163
  7. 2018Neural Importance Samplingcited by 154
  8. 2022NeILF: Neural Incident Light Field for Physically-based Material Estimationcited by 105
  9. 2021Real-time Neural Radiance Caching for Path Tracingcited by 91
  10. 2023NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimationcited by 73
  11. 2023Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmarkcited by 51
  12. 2023Neural Microfacet Fields for Inverse Renderingcited by 37
  13. 2023OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objectscited by 31
  14. 2024NeRF as a Non-Distant Environment Emitter in Physics-based Inverse Renderingcited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 5.

12 papers.