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NeLF-Pro: Neural Light Field Probes for Multi-Scale Novel View Synthesis (2312.13328v2)

Published 20 Dec 2023 in cs.CV

Abstract: We present NeLF-Pro, a novel representation to model and reconstruct light fields in diverse natural scenes that vary in extent and spatial granularity. In contrast to previous fast reconstruction methods that represent the 3D scene globally, we model the light field of a scene as a set of local light field feature probes, parameterized with position and multi-channel 2D feature maps. Our central idea is to bake the scene's light field into spatially varying learnable representations and to query point features by weighted blending of probes close to the camera - allowing for mipmap representation and rendering. We introduce a novel vector-matrix-matrix (VMM) factorization technique that effectively represents the light field feature probes as products of core factors (i.e., VM) shared among local feature probes, and a basis factor (i.e., M) - efficiently encoding internal relationships and patterns within the scene. Experimentally, we demonstrate that NeLF-Pro significantly boosts the performance of feature grid-based representations, and achieves fast reconstruction with better rendering quality while maintaining compact modeling. Project webpage https://sinoyou.github.io/nelf-pro/.

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References (72)
  1. Building rome in a day. In ICCV, 2009.
  2. Plenoxels: Radiance fields without neural networks. CVPR, 2022.
  3. Neural point-based graphics. arXiv.org, 1906.08240, 2019.
  4. Learning neural light fields with ray-space embedding networks. In CVPR, 2022.
  5. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In ICCV, 2021.
  6. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. 2022.
  7. Zip-nerf: Anti-aliased grid-based neural radiance fields. ICCV, 2023.
  8. Nerd: Neural reflectance decomposition from image collections. In ICCV, 2021a.
  9. Neural-pil: Neural pre-integrated lighting for reflectance decomposition. Advances in Neural Information Processing Systems, 2021b.
  10. Unstructured lumigraph rendering. In ACM Trans. on Graphics, 2001.
  11. Deep surface light fields. In ACM Trans. on Graphics, 2018.
  12. Tensorf: Tensorial radiance fields. In ECCV, 2022.
  13. Dictionary Fields: Learning a Neural Basis Decomposition. 2023a.
  14. Factor fields: A unified framework for neural fields and beyond. arXiv preprint arXiv:2302.01226, 2023b.
  15. Learning implicit fields for generative shape modeling. In CVPR, 2019.
  16. Online construction of surface light fields. In ESRT, 2005.
  17. The farthest point strategy for progressive image sampling. TIP, 1997.
  18. Strivec: Sparse tri-vector radiance fields. In ICCV, 2023.
  19. The lumigraph. In Seminal Graphics Papers: Pushing the Boundaries. 2023.
  20. Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In ICCV, 2023.
  21. Tensoir: Tensorial inverse rendering. In CVPR, 2023.
  22. Neural lumigraph rendering. In CVPR, 2021.
  23. 3d gaussian splatting for real-time radiance field rendering. ACM Trans. on Graphics, 2023.
  24. Adam: A method for stochastic optimization. In ICLR, 2015.
  25. Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation. In CVPR, 2022.
  26. Creating large-scale city models from 3d-point clouds: a robust approach with hybrid representation. International journal of computer vision, 2012.
  27. Light field rendering. In ACM Trans. on Graphics, pages 31–42, 1996.
  28. Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d. PAMI, 2022.
  29. Neural sparse voxel fields. In NeurIPS, 2020.
  30. Neural volumes: Learning dynamic renderable volumes from images. In ACM Trans. on Graphics, 2019.
  31. Nerf in the wild: Neural radiance fields for unconstrained photo collections. arXiv.org, 2008.02268, 2020.
  32. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. In CVPR, 2021.
  33. Real-time global illumination using precomputed light field probes. 2017.
  34. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019.
  35. Progressively optimized local radiance fields for robust view synthesis. In CVPR, 2023.
  36. Switch-nerf: Learning scene decomposition with mixture of experts for large-scale neural radiance fields. In ICLR, 2023.
  37. Local light field fusion: practical view synthesis with prescriptive sampling guidelines. In ACM Trans. on Graphics, 2019.
  38. NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020.
  39. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. on Graphics, 2022.
  40. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In CVPR, 2020.
  41. Learning implicit surface light fields. arXiv.org, 2003.12406, 2020.
  42. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019.
  43. Nerfmeshing: Distilling neural radiance fields into geometrically-accurate 3d meshes. arXiv.org, 2023.
  44. Ravi Ramamoorthi. Nerfs: The search for the best 3d representation. arXiv.org, 2023.
  45. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In ICCV, 2021.
  46. Robustnerf: Ignoring distractors with robust losses. In CVPR, 2023.
  47. Real-time global illumination by precomputed local reconstruction from sparse radiance probes. ACM Trans. on Graphics, 2017.
  48. Light field networks: Neural scene representations with single-evaluation rendering. In NeurIPS, 2021.
  49. Light field neural rendering. In CVPR, 2022.
  50. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. CVPR, 2022.
  51. Block-nerf: Scalable large scene neural view synthesis. In CVPR, 2022.
  52. Nerfstudio: A modular framework for neural radiance field development. In ACM Trans. on Graphics, 2023.
  53. Advances in neural rendering. In Computer Graphics Forum, 2022.
  54. Deferred neural rendering: image synthesis using neural textures. ACM Trans. on Graphics, 2019.
  55. Ledyard R Tucker. Some mathematical notes on three-mode factor analysis. Psychometrika, 1966.
  56. Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs. In CVPR, 2022.
  57. Ref-NeRF: Structured view-dependent appearance for neural radiance fields. 2022.
  58. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021a.
  59. F2-nerf: Fast neural radiance field training with free camera trajectories. 2023.
  60. Ibrnet: Learning multi-view image-based rendering. In CVPR, 2021b.
  61. Nex: Real-time view synthesis with neural basis expansion. In CVPR, 2021.
  62. Surface light fields for 3d photography. In ACM Trans. on Graphics, 2000.
  63. Bungeenerf: Progressive neural radiance field for extreme multi-scale scene rendering. In ECCV, 2022.
  64. Grid-guided neural radiance fields for large urban scenes. In CVPR, 2023.
  65. Point-nerf: Point-based neural radiance fields. In CVPR, 2022.
  66. Volume rendering of neural implicit surfaces. In NeurIPS, 2021.
  67. Sdfstudio: A unified framework for surface reconstruction, 2022a.
  68. Monosdf: Exploring monocular geometric cues for neural implicit surface reconstruction. In NeurIPS, 2022b.
  69. Nerf++: Analyzing and improving neural radiance fields. arXiv.org, 2010.07492, 2020.
  70. Physg: Inverse rendering with spherical gaussians for physics-based material editing and relighting. In CVPR, 2021.
  71. Modeling indirect illumination for inverse rendering. In CVPR, 2022.
  72. Stereo magnification: learning view synthesis using multiplane images. ACM Trans. on Graphics, 2018.
Citations (2)
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Summary

  • The paper introduces NeLF-Pro, a novel approach that uses local light field probes for flexible and efficient 3D scene reconstruction.
  • It employs a unique VMM decomposition and a soft localization strategy to compactly encode light field and geometric information.
  • Experimental results show that NeLF-Pro achieves fast, accurate, and memory-efficient rendering across large-scale, natural scenes.

Understanding Light Field Reconstruction with NeLF-Pro

Introduction to Light Field Probes

One of the frontiers in computer vision and graphics is the reconstruction of 3D scenes for creating realistic images from novel viewpoints, a process relevant in virtual reality, cinematography, and autonomous navigation. Recent advancements have brought Neural Radiance Fields (NeRF) into the spotlight, effectively capturing the complexities of real-world scenes with high fidelity. However, the reconstruction process with NeRF is notoriously slow.

Revamping Light Field Modeling

A novel approach, NeLF-Pro (Neural Light Field Probes), is changing the game by introducing local light field probes to represent scenes. Unlike traditional methods that capture scenes globally, NeLF-Pro deploys these probes locally, enabling flexible handling of scenes without being constrained by size or complexity. The method utilizes a new factorization technique, known as Vector-Matrix-Matrix (VMM) decomposition, to represent probes compactly. This effectively encodes light field and geometric information spatially across the scene.

Efficient Rendering and Reconstruction

NeLF-Pro can achieve fast scene reconstruction with remarkable rendering quality while maintaining a compact model footprint. The implementation utilizes a soft localization and blending algorithm to construct target images. It strategically selects a subset of probes based on their distance to the target camera, optimizing GPU memory and computational efficiency. This innovative querying process takes into account the spatial locality, thus supporting the training and rendering of extensive scenes without the necessity to load the entire scene's representation into memory.

Experimental Advantages

Extensive experiments reveal that NeLF-Pro can significantly enhance the performance of grid-based scene representations. The method attains fast and accurate reconstruction across a diverse array of natural scenes and scales. Results demonstrate that NeLF-Pro can produce novel view synthesis outcomes that are competitive with, or in some cases superior to, state-of-the-art methods, particularly in large-scale scenarios.

Advancing 3D Scene Understanding

NeLF-Pro's design to model the light field and geometry jointly allows for capturing highly accurate renditions of scenes with intricate details. The efficient representation method along with its capability to work with various spatial granularities makes it a promising step forward in the field of 3D scene reconstruction and rendering. While still in its early stages, this local probe-based approach to light field modeling has the potential to transform how we capture and visualize complex spatial environments, paving the way for more seamless and interactive virtual experiences.

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  1. NeLF-Pro
  2. GitHub - sinoyou/nelf-pro: repo for paper NeLF-Pro: Neural Light Field Probes (75 stars)
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