mlsanityData to 14 Oct 2026

Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines

arXiv 1905.00889 ↗First posted 2 May 2019cs.CV cs.GRcited by 672

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Cited by 34 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. We demonstrate our approach's practicality with an augmented reality smartphone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms.

Key facts. Cited by 672 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 May 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 66; 2022: 109; 2023: 208; 2024: 193; 2025: 92; 2026: 4.

672 papers.

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  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  2. 2021Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fieldscited by 2,032
  3. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 905
  4. 2023ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenescited by 764
  5. 2023K-Planes: Explicit Radiance Fields in Space, Time, and Appearancecited by 746
  6. 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 740
  7. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 673
  8. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 646
  9. 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 642
  10. 2021RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputscited by 615
  11. 2023Nerfstudio: A Modular Framework for Neural Radiance Field Developmentcited by 600
  12. 2023HexPlane: A Fast Representation for Dynamic Scenescited by 587
38 more
  1. 2023DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Visioncited by 537
  2. 2021Zero-Shot Text-Guided Object Generation with Dream Fieldscited by 513
  3. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 511
  4. 2023Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splattingcited by 476
  5. 2021Baking Neural Radiance Fields for Real-Time View Synthesiscited by 468
  6. 2021Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesiscited by 464
  7. 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Modelscited by 432
  8. 2021Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughscited by 398
  9. 2023Gaussian Grouping: Segment and Edit Anything in 3D Scenescited by 393
  10. 2022Decomposing NeRF for Editing via Feature Field Distillationcited by 371
  11. 2021Advances in Neural Renderingcited by 365
  12. 2023Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fieldscited by 364
  13. 2024GS-LRM: Large Reconstruction Model for 3D Gaussian Splattingcited by 349
  14. 2023Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesiscited by 344
  15. 2021Light Field Networks: Neural Scene Representations with Single-Evaluation Renderingcited by 332
  16. 2023ReconFusion: 3D Reconstruction with Diffusion Priorscited by 318
  17. 2022MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architecturescited by 311
  18. 2023FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularizationcited by 311
  19. 2023FSGS: Real-Time Few-shot View Synthesis using Gaussian Splattingcited by 279
  20. 2021Mixture of Volumetric Primitives for Efficient Neural Renderingcited by 277
  21. 2021Urban Radiance Fieldscited by 263
  22. 2024DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalizationcited by 256
  23. 2023SparseNeRF: Distilling Depth Ranking for Few-shot Novel View Synthesiscited by 247
  24. 2025AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Viewscited by 243
  25. 2022NoPe-NeRF: Optimising Neural Radiance Field with No Pose Priorcited by 235
  26. 2021DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networkscited by 232
  27. 2024A Survey on 3D Gaussian Splattingcited by 224
  28. 2021Editing Conditional Radiance Fieldscited by 222
  29. 2023MVImgNet: A Large-scale Dataset of Multi-view Imagescited by 220
  30. 2023MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenescited by 218
  31. 2023Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particlecited by 211
  32. 2021Self-Calibrating Neural Radiance Fieldscited by 204
  33. 2021Neural Articulated Radiance Fieldcited by 196
  34. 2022Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representationscited by 196
  35. 2021NerfingMVS: Guided Optimization of Neural Radiance Fields for Indoor Multi-view Stereocited by 192
  36. 2021Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenescited by 190
  37. 2021Point-Based Neural Rendering with Per-View Optimizationcited by 190
  38. 20244D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenescited by 189

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