mlsanityData to 7 Oct 2026

Neural Radiance Fields (NeRFs): A Review and Some Recent Developments

arXiv 2305.00375 ↗First posted 30 Apr 2023cs.CV eess.IVcited by 3

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Abstract (arXiv)

Neural Radiance Field (NeRF) is a framework that represents a 3D scene in the weights of a fully connected neural network, known as the Multi-Layer Perception(MLP). The method was introduced for the task of novel view synthesis and is able to achieve state-of-the-art photorealistic image renderings from a given continuous viewpoint. NeRFs have become a popular field of research as recent developments have been made that expand the performance and capabilities of the base framework. Recent developments include methods that require less images to train the model for view synthesis as well as methods that are able to generate views from unconstrained and dynamic scene representations.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 7 of its 10 references that are in the map. First posted on arXiv 30 Apr 2023.

Builds on

7 of 10 references.

References · 7

  1. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  2. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  3. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  4. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,574
  5. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  6. 2021RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputscited by 614
  7. 2021NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Imagescited by 336

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 1.

3 papers.