# ECRF: Entropy-Constrained Neural Radiance Fields Compression with Frequency Domain Optimization

arXiv: https://arxiv.org/abs/2311.14208 · HTML: https://mlsanity.com/paper/2311.14208/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 30 of 36)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [Variational image compression with a scale hyperprior](https://mlsanity.com/paper/1802.01436/) (2018)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [End-to-end Optimized Image Compression](https://mlsanity.com/paper/1611.01704/) (2016)
- [Joint Autoregressive and Hierarchical Priors for Learned Image Compression](https://mlsanity.com/paper/1809.02736/) (2018)
- [Neural Geometric Level of Detail: Real-time Rendering with Implicit 3D Shapes](https://mlsanity.com/paper/2101.10994/) (2021)
- [Advances in Neural Rendering](https://mlsanity.com/paper/2111.05849/) (2021)
- [High-Fidelity Generative Image Compression](https://mlsanity.com/paper/2006.09965/) (2020)
- [Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time](https://mlsanity.com/paper/2202.08614/) (2022)
- [Nonlinear Transform Coding](https://mlsanity.com/paper/2007.03034/) (2020)
- [Variable Bitrate Neural Fields](https://mlsanity.com/paper/2206.07707/) (2022)
- [Compressible-composable NeRF via Rank-residual Decomposition](https://mlsanity.com/paper/2205.14870/) (2022)
- [Neural Trajectory Fields for Dynamic Novel View Synthesis](https://mlsanity.com/paper/2105.05994/) (2021)
- [An Introduction to Neural Data Compression](https://mlsanity.com/paper/2202.06533/) (2022)
- [Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos](https://mlsanity.com/paper/2304.04452/) (2023)
- [Compressing Volumetric Radiance Fields to 1 MB](https://mlsanity.com/paper/2211.16386/) (2022)
- [Improved Direct Voxel Grid Optimization for Radiance Fields Reconstruction](https://mlsanity.com/paper/2206.05085/) (2022)
- [Masked Wavelet Representation for Compact Neural Radiance Fields](https://mlsanity.com/paper/2212.09069/) (2022)
- [Compressing Explicit Voxel Grid Representations: fast NeRFs become also small](https://mlsanity.com/paper/2210.12782/) (2022)

## Built on by (most cited first, 8 of 8)

- [Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs](https://mlsanity.com/paper/2501.03399/) (2025)
- [VRVVC: Variable-Rate NeRF-Based Volumetric Video Compression](https://mlsanity.com/paper/2412.11362/) (2024)
- [JointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field Representation and Compression](https://mlsanity.com/paper/2405.14452/) (2024)
- [HPC: Hierarchical Progressive Coding Framework for Volumetric Video](https://mlsanity.com/paper/2407.09026/) (2024)
- [Rate-aware Compression for NeRF-based Volumetric Video](https://mlsanity.com/paper/2411.05322/) (2024)
- [POTR: Post-Training 3DGS Compression](https://mlsanity.com/paper/2601.14821/) (2026)
- [CATRF: Codec-Adaptive TriPlane Radiance Fields for Volumetric Content Delivery](https://mlsanity.com/paper/2605.18054/) (2026)
- [Gaussian Splatting-based Volumetric Video Compression with Sparse 4D Anchors](https://mlsanity.com/paper/2609.33969/) (2026)

## Abstract (arXiv)

Explicit feature-grid based NeRF models have shown promising results in terms of rendering quality and significant speed-up in training. However, these methods often require a significant amount of data to represent a single scene or object. In this work, we present a compression model that aims to minimize the entropy in the frequency domain in order to effectively reduce the data size. First, we propose using the discrete cosine transform (DCT) on the tensorial radiance fields to compress the feature-grid. This feature-grid is transformed into coefficients, which are then quantized and entropy encoded, following a similar approach to the traditional video coding pipeline. Furthermore, to achieve a higher level of sparsity, we propose using an entropy parameterization technique for the frequency domain, specifically for DCT coefficients of the feature-grid. Since the transformed coefficients are optimized during the training phase, the proposed model does not require any fine-tuning or additional information. Our model only requires a lightweight compression pipeline for encoding and decoding, making it easier to apply volumetric radiance field methods for real-world applications. Experimental results demonstrate that our proposed frequency domain entropy model can achieve superior compression performance across various datasets. The source code will be made publicly available.