# One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation

arXiv: https://arxiv.org/abs/2211.15977 · HTML: https://mlsanity.com/paper/2211.15977/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 37 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [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)
- [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [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)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [Decomposing NeRF for Editing via Feature Field Distillation](https://mlsanity.com/paper/2205.15585/) (2022)
- [NeX: Real-time View Synthesis with Neural Basis Expansion](https://mlsanity.com/paper/2103.05606/) (2021)
- [Vision-Only Robot Navigation in a Neural Radiance World](https://mlsanity.com/paper/2110.00168/) (2021)
- [LENS: Localization enhanced by NeRF synthesis](https://mlsanity.com/paper/2110.06558/) (2021)
- [Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images](https://mlsanity.com/paper/2007.09892/) (2020)
- [Compressible-composable NeRF via Rank-residual Decomposition](https://mlsanity.com/paper/2205.14870/) (2022)
- [R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis](https://mlsanity.com/paper/2203.17261/) (2022)
- [DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic Integration for Volume Rendering](https://mlsanity.com/paper/2111.10427/) (2021)
- [MegLoc: A Robust and Accurate Visual Localization Pipeline](https://mlsanity.com/paper/2111.13063/) (2021)
- [PERF: Performant, Explicit Radiance Fields](https://mlsanity.com/paper/2112.05598/) (2021)
- [T4DT: Tensorizing Time for Learning Temporal 3D Visual Data](https://mlsanity.com/paper/2208.01421/) (2022)

## Built on by (most cited first, 18 of 18)

- [Chat-Edit-3D: Interactive 3D Scene Editing via Text Prompts](https://mlsanity.com/paper/2407.06842/) (2024)
- [AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis](https://mlsanity.com/paper/2402.17483/) (2024)
- [NeRF Is a Valuable Assistant for 3D Gaussian Splatting](https://mlsanity.com/paper/2507.23374/) (2025)
- [Free-Editor: Zero-shot Text-driven 3D Scene Editing](https://mlsanity.com/paper/2312.13663/) (2023)
- [StyleRF-VolVis: Style Transfer of Neural Radiance Fields for Expressive Volume Visualization](https://mlsanity.com/paper/2408.00150/) (2024)
- [Editing 3D Scenes via Text Prompts without Retraining](https://mlsanity.com/paper/2309.04917/) (2023)
- [SCARF: Scalable Continual Learning Framework for Memory-efficient Multiple Neural Radiance Fields](https://mlsanity.com/paper/2409.04482/) (2024)
- [Dropping Anchor and Spherical Harmonics for Sparse-view Gaussian Splatting](https://mlsanity.com/paper/2602.20933/) (2026)
- [Progressive Volume Distillation with Active Learning for Efficient NeRF Architecture Conversion](https://mlsanity.com/paper/2304.04012/) (2023)
- [Efficient Neural Video Representation with Temporally Coherent Modulation](https://mlsanity.com/paper/2505.00335/) (2025)
- [DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting](https://mlsanity.com/paper/2604.06739/) (2026)
- [FusedRF: Fusing Multiple Radiance Fields](https://mlsanity.com/paper/2306.04180/) (2023)
- [WaterClear-GS: Optical-Aware Gaussian Splatting for Underwater Reconstruction and Restoration](https://mlsanity.com/paper/2601.19753/) (2026)
- [Chat-Edit-3D++: Interactive 3D and 4D Scene Editing via Large Language Models](https://mlsanity.com/paper/2608.29137/) (2026)
- [Incremental Multi-Scene Modeling via Continual Neural Graphics Primitives](https://mlsanity.com/paper/2411.19903/) (2024)
- [RouteBridge: Reliability-Routed Bidirectional Distillation Between Neural Radiance Fields and 3D Gaussian Splatting](https://mlsanity.com/paper/2609.09606/) (2026)
- [Is Gaussian Splatting Becoming Neural Again? A Taxonomy and Controlled Study of Learned Parameterization](https://mlsanity.com/paper/2609.12395/) (2026)
- [What Makes a 3D Scene Editable? A Factorized Benchmark of Fidelity, Locality, Consistency, and Preservation](https://mlsanity.com/paper/2609.14899/) (2026)

## Abstract (arXiv)

Neural Radiance Fields (NeRF) methods have proved effective as compact, high-quality and versatile representations for 3D scenes, and enable downstream tasks such as editing, retrieval, navigation, etc. Various neural architectures are vying for the core structure of NeRF, including the plain Multi-Layer Perceptron (MLP), sparse tensors, low-rank tensors, hashtables and their compositions. Each of these representations has its particular set of trade-offs. For example, the hashtable-based representations admit faster training and rendering but their lack of clear geometric meaning hampers downstream tasks like spatial-relation-aware editing. In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions. PVD consequently empowers downstream applications to optimally adapt the neural representations for the task at hand in a post hoc fashion. The conversions are fast, as distillation is progressively performed on different levels of volume representations, from shallower to deeper. We also employ special treatment of density to deal with its specific numerical instability problem. Empirical evidence is presented to validate our method on the NeRF-Synthetic, LLFF and TanksAndTemples datasets. For example, with PVD, an MLP-based NeRF model can be distilled from a hashtable-based Instant-NGP model at a 10X~20X faster speed than being trained the original NeRF from scratch, while achieving a superior level of synthesis quality. Code is available at https://github.com/megvii-research/AAAI2023-PVD.