mlsanityData to 7 Oct 2026

Neural Radiance Fields for the Real World: A Survey

arXiv 2501.13104 ↗First posted 22 Jan 2025cs.CV cs.GRcited by 12

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Abstract (arXiv)

Neural Radiance Fields (NeRFs) have remodeled 3D scene representation since release. NeRFs can effectively reconstruct complex 3D scenes from 2D images, advancing different fields and applications such as scene understanding, 3D content generation, and robotics. Despite significant research progress, a thorough review of recent innovations, applications, and challenges is lacking. This survey compiles key theoretical advancements and alternative scene representations and investigates emerging challenges. It further explores applications on reconstruction, highlights NeRFs' impact on computer vision and robotics, and reviews essential datasets and toolkits. By identifying gaps in the literature, this survey discusses open challenges and offers directions for future research.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 234 of its 301 references that are in the map. First posted on arXiv 22 Jan 2025.

Builds on

234 of 301 references.

References · 234

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38 more
  1. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
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  4. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  5. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
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  13. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
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  15. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
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  17. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  18. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
  19. 2024Grounding Image Matching in 3D with MASt3Rcited by 960
  20. 2020Neural Sparse Voxel Fieldscited by 959
  21. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
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50 most cited of 234 All 234 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 7.

12 papers.

  1. 2026Uni-World VLA: Interleaved World Modeling and Planning for Autonomous Drivingcited by 9
  2. 2026DAV-GSWT: Diffusion-Active-View Sampling for Data-Efficient Gaussian Splatting Wang Tilescited by 2
  3. 2025Structural Energy-Guided Sampling for View-Consistent Text-to-3Dcited by 1
  4. 2025From Fields to Splats: A Cross-Domain Survey of Real-Time Neural Scene Representationscited by 1
  5. 2025Multispectral-NeRF:a multispectral modeling approach based on neural radiance fieldscited by 0
  6. 2025DT-NeRF: A Diffusion and Transformer-Based Optimization Approach for Neural Radiance Fields in 3D Reconstructioncited by 0
  7. 2025SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Renderingcited by 0
  8. 2026SwiftGS: Episodic Priors for Immediate Satellite Surface Recoverycited by 0
  9. 2026First Shape, Then Meaning: Efficient Geometry and Semantics Learning for Indoor Reconstructioncited by 0
  10. 20263D Gaussian Splatting for Efficient Retrospective Dynamic Scene Novel View Synthesis with a Standardized Benchmarkcited by 0
  11. 2026Structural Energy Guidance for View-Consistent Text-to-3D Generationcited by 0
  12. 2026CLEAR-NeRF: Collinearity and Local-region Enhanced Accurate 3D Reconstruction in Unbounded Scenescited by 0