# Recursive-NeRF: An Efficient and Dynamically Growing NeRF

arXiv: https://arxiv.org/abs/2105.09103 · HTML: https://mlsanity.com/paper/2105.09103/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 65 references that are in the map.
- First posted on arXiv 19 May 2021.

## Builds on (most cited first, 28 of 28)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis](https://mlsanity.com/paper/2012.03927/) (2020)
- [Neural 3D Mesh Renderer](https://mlsanity.com/paper/1711.07566/) (2017)
- [Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning](https://mlsanity.com/paper/1904.01786/) (2019)
- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [Local Implicit Grid Representations for 3D Scenes](https://mlsanity.com/paper/2003.08981/) (2020)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020)
- [Universally Slimmable Networks and Improved Training Techniques](https://mlsanity.com/paper/1903.05134/) (2019)
- [Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer](https://mlsanity.com/paper/1908.01210/) (2019)
- [AutoInt: Automatic Integration for Fast Neural Volume Rendering](https://mlsanity.com/paper/2012.01714/) (2020)
- [DeRF: Decomposed Radiance Fields](https://mlsanity.com/paper/2011.12490/) (2020)
- [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020)
- [A Survey on Deep Geometry Learning: From a Representation Perspective](https://mlsanity.com/paper/2002.07995/) (2020)
- [VoronoiNet: General Functional Approximators with Local Support](https://mlsanity.com/paper/1912.03629/) (2019)

## Built on by (most cited first, 30 of 32)

- [Neural Fields in Visual Computing and Beyond](https://mlsanity.com/paper/2111.11426/) (2021)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)](https://mlsanity.com/paper/2210.00379/) (2022)
- [NeRF-Art: Text-Driven Neural Radiance Fields Stylization](https://mlsanity.com/paper/2212.08070/) (2022)
- [A Large-Scale Outdoor Multi-modal Dataset and Benchmark for Novel View Synthesis and Implicit Scene Reconstruction](https://mlsanity.com/paper/2301.06782/) (2023)
- [NeRFLiX: High-Quality Neural View Synthesis by Learning a Degradation-Driven Inter-viewpoint MiXer](https://mlsanity.com/paper/2303.06919/) (2023)
- [Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fields](https://mlsanity.com/paper/2211.12285/) (2022)
- [Neural Radiance Field-based Visual Rendering: A Comprehensive Review](https://mlsanity.com/paper/2404.00714/) (2024)
- [Neural Radiance Fields: Past, Present, and Future](https://mlsanity.com/paper/2304.10050/) (2023)
- [Incorporating Season and Solar Specificity into Renderings made by a NeRF Architecture using Satellite Images](https://mlsanity.com/paper/2308.01262/) (2023)
- [From NeRFLiX to NeRFLiX++: A General NeRF-Agnostic Restorer Paradigm](https://mlsanity.com/paper/2306.06388/) (2023)
- [Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation](https://mlsanity.com/paper/2401.14354/) (2024)
- [Content-Aware Radiance Fields: Aligning Model Complexity with Scene Intricacy Through Learned Bitwidth Quantization](https://mlsanity.com/paper/2410.19483/) (2024)
- [ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes](https://mlsanity.com/paper/2212.06193/) (2022)
- [High-Fidelity Eye Animatable Neural Radiance Fields for Human Face](https://mlsanity.com/paper/2308.00773/) (2023)
- [MS-NeRF: Multi-Space Neural Radiance Fields](https://mlsanity.com/paper/2305.04268/) (2023)
- [Progressive Multi-scale Light Field Networks](https://mlsanity.com/paper/2208.06710/) (2022)
- [SLS4D: Sparse Latent Space for 4D Novel View Synthesis](https://mlsanity.com/paper/2312.09743/) (2023)
- [TextGaze: Gaze-Controllable Face Generation with Natural Language](https://mlsanity.com/paper/2404.17486/) (2024)
- [Aerial-NeRF: Adaptive Spatial Partitioning and Sampling for Large-Scale Aerial Rendering](https://mlsanity.com/paper/2405.06214/) (2024)
- [NOVA-3D: Non-overlapped Views for 3D Anime Character Reconstruction](https://mlsanity.com/paper/2405.12505/) (2024)
- [ReFiNe: Recursive Field Networks for Cross-modal Multi-scene Representation](https://mlsanity.com/paper/2406.04309/) (2024)
- [CD-NGP: A Fast Scalable Continual Representation for Dynamic Scenes](https://mlsanity.com/paper/2409.05166/) (2024)
- [MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field](https://mlsanity.com/paper/2309.13607/) (2023)
- [GaussEdit: Adaptive 3D Scene Editing with Text and Image Prompts](https://mlsanity.com/paper/2509.26055/) (2025)
- [EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction](https://mlsanity.com/paper/2311.09806/) (2023)
- [T-Code: Simple Temporal Latent Code for Efficient Dynamic View Synthesis](https://mlsanity.com/paper/2312.11015/) (2023)
- [NeRF-VPT: Learning Novel View Representations with Neural Radiance Fields via View Prompt Tuning](https://mlsanity.com/paper/2403.01325/) (2024)
- [LGSDF: Continual Global Learning of Signed Distance Fields Aided by Local Updating](https://mlsanity.com/paper/2404.05187/) (2024)
- [High-Fidelity Mask-free Neural Surface Reconstruction for Virtual Reality](https://mlsanity.com/paper/2409.13158/) (2024)

## Abstract (arXiv)

View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attention due to their high quality imagery and scalability to high resolution. However, the heavy computation required by its volumetric approach prevents NeRF from being useful in practice; minutes are taken to render a single image of a few megapixels. Now, an image of a scene can be rendered in a level-of-detail manner, so we posit that a complicated region of the scene should be represented by a large neural network while a small neural network is capable of encoding a simple region, enabling a balance between efficiency and quality. Recursive-NeRF is our embodiment of this idea, providing an efficient and adaptive rendering and training approach for NeRF. The core of Recursive-NeRF learns uncertainties for query coordinates, representing the quality of the predicted color and volumetric intensity at each level. Only query coordinates with high uncertainties are forwarded to the next level to a bigger neural network with a more powerful representational capability. The final rendered image is a composition of results from neural networks of all levels. Our evaluation on three public datasets shows that Recursive-NeRF is more efficient than NeRF while providing state-of-the-art quality. The code will be available at https://github.com/Gword/Recursive-NeRF.