# Locality-aware Gaussian Compression for Fast and High-quality Rendering

arXiv: https://arxiv.org/abs/2501.05757 · HTML: https://mlsanity.com/paper/2501.05757/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 40 references that are in the map.
- First posted on arXiv 10 Jan 2025.

## Builds on (most cited first, 30 of 33)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [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)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction](https://mlsanity.com/paper/2206.00665/) (2022)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [Movement Pruning: Adaptive Sparsity by Fine-Tuning](https://mlsanity.com/paper/2005.07683/) (2020)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction](https://mlsanity.com/paper/2212.05231/) (2022)
- [MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures](https://mlsanity.com/paper/2208.00277/) (2022)
- [Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis](https://mlsanity.com/paper/2401.02436/) (2023)
- [COIN: COmpression with Implicit Neural representations](https://mlsanity.com/paper/2103.03123/) (2021)
- [MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes](https://mlsanity.com/paper/2302.12249/) (2023)
- [HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression](https://mlsanity.com/paper/2403.14530/) (2024)
- [EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS](https://mlsanity.com/paper/2312.04564/) (2023)
- [Implicit Neural Representations for Image Compression](https://mlsanity.com/paper/2112.04267/) (2021)
- [Compact 3D Scene Representation via Self-Organizing Gaussian Grids](https://mlsanity.com/paper/2312.13299/) (2023)
- [CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization](https://mlsanity.com/paper/2311.18159/) (2023)
- [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)

## Built on by (most cited first, 25 of 25)

- [PCGS: Progressive Compression of 3D Gaussian Splatting](https://mlsanity.com/paper/2503.08511/) (2025)
- [Optimized Minimal 3D Gaussian Splatting](https://mlsanity.com/paper/2503.16924/) (2025)
- [Mobile-GS: Real-time Gaussian Splatting for Mobile Devices](https://mlsanity.com/paper/2603.11531/) (2026)
- [ReCon-GS: Continuum-Preserved Gaussian Streaming for Fast and Compact Reconstruction of Dynamic Scenes](https://mlsanity.com/paper/2509.24325/) (2025)
- [CF3: Compact and Fast 3D Feature Fields](https://mlsanity.com/paper/2508.05254/) (2025)
- [Gaussian Herding across Pens: An Optimal Transport Perspective on Global Gaussian Reduction for 3DGS](https://mlsanity.com/paper/2506.09534/) (2025)
- [ForestSplats: Deformable transient field for Gaussian Splatting in the Wild](https://mlsanity.com/paper/2503.06179/) (2025)
- [MEGS2: Memory-Efficient Gaussian Splatting via Spherical Gaussians and Unified Pruning](https://mlsanity.com/paper/2509.07021/) (2025)
- [Optimized Minimal 4D Gaussian Splatting](https://mlsanity.com/paper/2510.03857/) (2025)
- [CAT-3DGS Pro: A New Benchmark for Efficient 3DGS Compression](https://mlsanity.com/paper/2503.12862/) (2025)
- [POTR: Post-Training 3DGS Compression](https://mlsanity.com/paper/2601.14821/) (2026)
- [GaussianPOP: Principled Simplification Framework for Compact 3D Gaussian Splatting via Error Quantification](https://mlsanity.com/paper/2602.06830/) (2026)
- [HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis](https://mlsanity.com/paper/2509.17083/) (2025)
- [Learning Unified Representation of 3D Gaussian Splatting](https://mlsanity.com/paper/2509.22917/) (2025)
- [Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting](https://mlsanity.com/paper/2606.30017/) (2026)
- [GSta: Efficient Training Scheme with Siestaed Gaussians for Monocular 3D Scene Reconstruction](https://mlsanity.com/paper/2504.06716/) (2025)
- [Learning Hierarchical Sparse Transform Coding for 3DGS Compression](https://mlsanity.com/paper/2505.22908/) (2025)
- [Leveraging Learned Image Prior for 3D Gaussian Compression](https://mlsanity.com/paper/2510.14705/) (2025)
- [ProGS: Towards Progressive Coding for 3D Gaussian Splatting](https://mlsanity.com/paper/2603.09703/) (2026)
- [LG-HCC: Local Geometry-Aware Hierarchical Context Compression for 3D Gaussian Splatting](https://mlsanity.com/paper/2603.28431/) (2026)
- [Unfolding 3D Gaussian Splatting via Iterative Gaussian Synopsis](https://mlsanity.com/paper/2604.11685/) (2026)
- [MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching](https://mlsanity.com/paper/2604.26799/) (2026)
- [3DGS3: Joint Super Sampling and Frame Interpolation for Real-Time Large-Scale 3DGS Rendering](https://mlsanity.com/paper/2605.11489/) (2026)
- [Rectifying Mask via Entropy for Distractor-Free 3DGS in Ambiguous Scenarios](https://mlsanity.com/paper/2606.29496/) (2026)
- [Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting](https://mlsanity.com/paper/2610.05289/) (2026)

## Abstract (arXiv)

We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric scenes. To this end, we first analyze the local coherence of 3D Gaussian attributes, and propose a novel locality-aware 3D Gaussian representation that effectively encodes locally-coherent Gaussian attributes using a neural field representation with a minimal storage requirement. On top of the novel representation, LocoGS is carefully designed with additional components such as dense initialization, an adaptive spherical harmonics bandwidth scheme and different encoding schemes for different Gaussian attributes to maximize compression performance. Experimental results demonstrate that our approach outperforms the rendering quality of existing compact Gaussian representations for representative real-world 3D datasets while achieving from 54.6× to 96.6× compressed storage size and from 2.1× to 2.4× rendering speed than 3DGS. Even our approach also demonstrates an averaged 2.4× higher rendering speed than the state-of-the-art compression method with comparable compression performance.