Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

SpecNeRF: Gaussian Directional Encoding for Specular Reflections (2312.13102v3)

Published 20 Dec 2023 in cs.CV

Abstract: Neural radiance fields have achieved remarkable performance in modeling the appearance of 3D scenes. However, existing approaches still struggle with the view-dependent appearance of glossy surfaces, especially under complex lighting of indoor environments. Unlike existing methods, which typically assume distant lighting like an environment map, we propose a learnable Gaussian directional encoding to better model the view-dependent effects under near-field lighting conditions. Importantly, our new directional encoding captures the spatially-varying nature of near-field lighting and emulates the behavior of prefiltered environment maps. As a result, it enables the efficient evaluation of preconvolved specular color at any 3D location with varying roughness coefficients. We further introduce a data-driven geometry prior that helps alleviate the shape radiance ambiguity in reflection modeling. We show that our Gaussian directional encoding and geometry prior significantly improve the modeling of challenging specular reflections in neural radiance fields, which helps decompose appearance into more physically meaningful components.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (63)
  1. Agisoft LLC. Agisoft Metashape Professional. Computer software, 2022.
  2. Inverse path tracing for joint material and lighting estimation. In CVPR, 2019.
  3. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In CVPR, 2022.
  4. ZoeDepth: Zero-shot transfer by combining relative and metric depth. arXiv:2302.12288, 2023.
  5. Neural reflectance fields for appearance acquisition. arXiv:2008.03824, 2020.
  6. NeRD: Neural reflectance decomposition from image collections. In ICCV, 2021a.
  7. Neural-PIL: Neural pre-integrated lighting for reflectance decomposition. In NeurIPS, 2021b.
  8. G. Bradski. The OpenCV Library. Dr. Dobb’s Journal of Software Tools, 2000.
  9. TensoRF: Tensorial radiance fields. In ECCV, 2022.
  10. A reflectance model for computer graphics. ACM Trans. Graph., 1(1):7–24, 1982.
  11. DIP: Differentiable interreflection-aware physics-based inverse rendering. In 3DV, 2024.
  12. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3D scans. In ICCV, 2021.
  13. Ref-NeuS: Ambiguity-reduced neural implicit surface learning for multi-view reconstruction with reflection. In ICCV, 2023.
  14. NeRFReN: Neural radiance fields with reflections. In CVPR, 2022.
  15. TraM-NeRF: Tracing mirror and near-perfect specular reflections through neural radiance fields. arXiv:2310.10650, 2023.
  16. James T. Kajiya. The rendering equation. Computer Graphics (Proceedings of SIGGRAPH), 20(4):143–150, 1986.
  17. 3D Gaussian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4):139:1–14, 2023.
  18. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
  19. Neural point catacaustics for novel-view synthesis of reflections. ACM Trans. Graph., 41(6):201:1–15, 2022.
  20. Neural reflectance for shape recovery with shadow handling. In CVPR, 2022.
  21. NeuLighting: Neural lighting for free viewpoint outdoor scene relighting with unconstrained photo collections. In SIGGRAPH Asia, pages 13:1–9, 2022.
  22. Differentiable Monte Carlo ray tracing through edge sampling. ACM Trans. Graph., 37(6):222:1–11, 2018.
  23. Neuralangelo: High-fidelity neural surface reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
  24. SPIDR: SDF-based neural point fields for illumination and deformation. arXiv:2210.08398, 2022.
  25. Clean-NeRF: Reformulating NeRF to account for view-dependent observations. arXiv:2303.14707, 2023a.
  26. NeRO: Neural geometry and BRDF reconstruction of reflective objects from multiview images. ACM Trans. Graph., pages 114:1–22, 2023b.
  27. Neural radiance transfer fields for relightable novel-view synthesis with global illumination. In ECCV, 2022.
  28. Neural microfacet fields for inverse rendering. In ICCV, 2023.
  29. NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020.
  30. Illumination and reflection maps. In ACM SIGGRAPH, 1984.
  31. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1–15, 2022.
  32. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems 32, pages 8024–8035. Curran Associates, Inc., 2019.
  33. Free-viewpoint indoor neural relighting from multi-view stereo. ACM Trans. Graph., 40(5):194:1–18, 2021.
  34. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. TPAMI, 44(3):1623–1637, 2022.
  35. NeRF for outdoor scene relighting. In ECCV, 2022.
  36. NeRV: Neural reflectance and visibility fields for relighting and view synthesis. In CVPR, 2021.
  37. Fourier features let networks learn high frequency functions in low dimensional domains. In NeurIPS, 2020.
  38. Nerfstudio: A modular framework for neural radiance field development. In SIGGRAPH, pages 72:1–12, 2023.
  39. Advances in neural rendering. Comput. Graph. Forum, 41(2):703–735, 2022.
  40. ORCa: Glossy objects as radiance field cameras. In CVPR, 2023.
  41. Ref-NeRF: Structured view-dependent appearance for neural radiance fields. In CVPR, 2022.
  42. Neural fields meet explicit geometric representations for inverse rendering of urban scenes. In CVPR, 2023.
  43. Factorized inverse path tracing for efficient and accurate material-lighting estimation. In ICCV, 2023a.
  44. Factorized inverse path tracing for efficient and accurate material-lighting estimation. In ICCV, 2023b.
  45. Scalable neural indoor scene rendering. ACM Trans. Graph., 41(4):98:1–16, 2022.
  46. Neural fields in visual computing and beyond. Comput. Graph. Forum, 2022.
  47. Scalable image-based indoor scene rendering with reflections. ACM Trans. Graph., 40(4):60:1–14, 2021.
  48. VR-NeRF: High-fidelity virtualized walkable spaces. In SIGGRAPH Asia, 2023.
  49. NeRF-DS: Neural radiance fields for dynamic specular objects. In CVPR, 2023.
  50. Freenerf: Improving few-shot neural rendering with free frequency regularization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8254–8263, 2023.
  51. NeILF: Neural incident light field for physically-based material estimation. In ECCV, 2022.
  52. Multi-space neural radiance fields. In CVPR, 2023.
  53. MILO: Multi-bounce inverse rendering for indoor scene with light-emitting objects. IEEE TPAMI, 45(8):10129–10142, 2023.
  54. MonoSDF: Exploring monocular geometric cues for neural implicit surface reconstruction. In NeurIPS, 2022.
  55. Relighting neural radiance fields with shadow and highlight hints. In SIGGRAPH, 2023a.
  56. Mirror-NeRF: Learning neural radiance fields for mirrors with Whitted-style ray tracing. In ACM Multimedia, 2023b.
  57. NeILF++: Inter-reflectable light fields for geometry and material estimation. In ICCV, 2023a.
  58. PhySG: Inverse rendering with spherical gaussians for physics-based material editing and relighting. In CVPR, 2021a.
  59. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018.
  60. NeRFactor: Neural factorization of shape and reflectance under an unknown illumination. ACM Trans. Graph., 40(6):237:1–18, 2021b.
  61. Modeling indirect illumination for inverse rendering. In CVPR, 2022.
  62. NeMF: Inverse volume rendering with neural microflake field. arXiv:2304.00782, 2023b.
  63. NeAI: A pre-convoluted representation for plug-and-play neural ambient illumination. arXiv:2304.08757, 2023.
Citations (13)
View on Semantic Scholar

Summary

  • The paper introduces a novel Gaussian directional encoding method that effectively captures realistic specular reflections in 3D scene reconstructions.
  • It overcomes traditional NeRF limitations by modeling near-field lighting and using monocular normal estimation to resolve view-dependent ambiguities.
  • Experiments demonstrate state-of-the-art novel view synthesis on glossy surfaces, promising enhanced applications in VR and realistic scene editing.

Understanding SpecNeRF: Capturing Specular Reflections in 3D Scene Reconstructions

Introduction to Neural Radiance Fields (NeRFs)

NeRFs have become quite popular as a way to represent 3D scenes, particularly for creating new views of a captured scene. They work by training a neural network with a set of images and corresponding viewpoints. This network then figures out a representation that can predict what an image would look like from any new viewpoint. This technology has shown impressive results, especially with room-scale scenes.

Challenges with Glossy Surfaces

However, one hiccup in this technology has been correctly representing glossy or shiny surfaces, such as reflections on a polished floor or a glass window. Most NeRFs can handle mild changes in appearance due to shifting viewpoints well, but as the surface gets glossier, capturing the high-frequency changes in reflection becomes challenging. Traditionally, some NeRFs try to "fake" these reflections by placing them under the surface textures, which can result in incorrect views or "foggy" geometries upon closer examination.

SpecNeRF's Novel Approach

The innovation in SpecNeRF lies in its ability to understand near-field lighting, which refers to the way light behaves when it's close to objects in a scene. Existing methods were falling short because they used certain mathematical functions that assumed light came from far away, which is not always true indoors.

To address this, SpecNeRF introduces a new way of encoding the behavior of light rays using something called Gaussian directional encoding. These are mathematically tractable functions that are shaped like 3D bells and can vary their shape and size to represent complex lighting behaviors around objects.

Impact on Spatially-Varying Lighting

SpecNeRF's encoding can adapt to spatial changes in lighting, enabling it to capture reflections and how they change as one moves around in a 3D scene. Think of it as translating the intricate dance of light and reflection into a language that a NeRF model can understand and replicate realistically, even when the lighting conditions aren't uniform.

Practical Results and Contributions

The experiments show that SpecNeRF does a better job at rendering glossy surfaces in comparison to existing NeRF methods. Another benefit of SpecNeRF is that it could infer meaningful physical components of a scene, such as how shiny a surface is, from the sheer way light reflects off it. This could have applications beyond mere view synthesis, such as in virtual reality, where one might want to edit the appearance of a 3D object or manipulate objects within a realistic lighting situation.

In summary, SpecNeRF's key contributions include:

  • A novel encoding to handle specular reflection modeling in various lighting conditions.
  • Utilizing monocular normal estimation to overcome ambiguities in shape and radiance early in training.
  • Achieving state-of-the-art results in synthesizing novel views, especially for scenes with shiny reflections.

SpecNeRF represents a significant step towards handling the nuances of light within neural radiance fields, allowing for more photorealistic and accurate renderings of complex, real-world scenes.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown