mlsanityData to 14 Oct 2026

ScanNeRF: a Scalable Benchmark for Neural Radiance Fields

arXiv 2211.13762 ↗First posted 24 Nov 2022cs.CVcited by 14

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Abstract (arXiv)

In this paper, we propose the first-ever real benchmark thought for evaluating Neural Radiance Fields (NeRFs) and, in general, Neural Rendering (NR) frameworks. We design and implement an effective pipeline for scanning real objects in quantity and effortlessly. Our scan station is built with less than 500 hardware budget and can collect roughly 4000 images of a scanned object in just 5 minutes. Such a platform is used to build ScanNeRF, a dataset characterized by several train/val/test splits aimed at benchmarking the performance of modern NeRF methods under different conditions. Accordingly, we evaluate three cutting-edge NeRF variants on it to highlight their strengths and weaknesses. The dataset is available on our project page, together with an online benchmark to foster the development of better and better NeRFs.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 43 of its 54 references that are in the map. First posted on arXiv 24 Nov 2022.

Builds on

43 of 54 references.

References · 43

  1. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  2. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
  3. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  4. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,964
  5. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,613
  6. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,584
  7. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
  8. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,232
  9. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 964
  10. 2020Neural Sparse Voxel Fieldscited by 959
  11. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 905
  12. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 751
31 more
  1. 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 740
  2. 2021Depth-supervised NeRF: Fewer Views and Faster Training for Freecited by 735
  3. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  4. 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 703
  5. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  6. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 673
  7. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  8. 2021Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstructioncited by 652
  9. 2021FastNeRF: High-Fidelity Neural Rendering at 200FPScited by 511
  10. 2020NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesiscited by 496
  11. 2019BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networkscited by 490
  12. 2021Baking Neural Radiance Fields for Real-Time View Synthesiscited by 468
  13. 2020Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstructioncited by 467
  14. 2020NeRD: Neural Reflectance Decomposition from Image Collectionscited by 449
  15. 2021Dynamic View Synthesis from Dynamic Monocular Videocited by 420
  16. 2020Space-time Neural Irradiance Fields for Free-Viewpoint Videocited by 414
  17. 2018DeepVoxels: Learning Persistent 3D Feature Embeddingscited by 405
  18. 2021Advances in Neural Renderingcited by 365
  19. 2020Single-View View Synthesis with Multiplane Imagescited by 263
  20. 2019DeepView: View Synthesis with Learned Gradient Descentcited by 253
  21. 2021NeX: Real-time View Synthesis with Neural Basis Expansioncited by 216
  22. 2021HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fieldscited by 214
  23. 2018Stereo Magnification: Learning View Synthesis using Multiplane Imagescited by 211
  24. 2021Neural Articulated Radiance Fieldcited by 196
  25. 2021Neural Rays for Occlusion-aware Image-based Renderingcited by 192
  26. 2019Pushing the Boundaries of View Extrapolation with Multiplane Imagescited by 191
  27. 2021Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenescited by 190
  28. 2019Neural Volumes: Learning Dynamic Renderable Volumes from Imagescited by 173
  29. 2021NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illuminationcited by 135
  30. 2021NeRF-VAE: A Geometry Aware 3D Scene Generative Modelcited by 112
  31. 2020Crowdsampling the Plenoptic Functioncited by 62

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Citing papers per year: 2023: 7; 2024: 4; 2025: 3.

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  1. 2023LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPScited by 436
  2. 2024Large Spatial Model: End-to-end Unposed Images to Semantic 3Dcited by 73
  3. 2023Perceptual Quality Assessment of NeRF and Neural View Synthesis Methods for Front-Facing Viewscited by 17
  4. 2023DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fieldscited by 17
  5. 2024Parameter-efficient Prompt Learning for 3D Point Cloud Understandingcited by 16
  6. 2023The Age of Synthetic Realities: Challenges and Opportunitiescited by 15
  7. 2025SpatialSplat: Efficient Semantic 3D from Sparse Unposed Imagescited by 13
  8. 2023Leveraging VLM-Based Pipelines to Annotate 3D Objectscited by 10
  9. 2024Connecting NeRFs, Images, and Textcited by 6
  10. 2024CrossScore: Towards Multi-View Image Evaluation and Scoringcited by 6
  11. 2023Towards a Robust Framework for NeRF Evaluationcited by 2
  12. 2023Towards Visual Foundational Models of Physical Scenescited by 2
2 more
  1. 2025Weight Space Representation Learning on Diverse NeRF Architecturescited by 0
  2. 2025AH-GS: Augmented 3D Gaussian Splatting for High-Frequency Detail Representationcited by 0