# GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for Compact and High-Quality 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2411.06019 · HTML: https://mlsanity.com/paper/2411.06019/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 56 references that are in the map.
- First posted on arXiv 9 Nov 2024.

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

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [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)
- [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)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis](https://mlsanity.com/paper/2401.02436/) (2023)
- [Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians](https://mlsanity.com/paper/2403.17898/) (2024)
- [A Survey on 3D Gaussian Splatting](https://mlsanity.com/paper/2401.03890/) (2024)
- [Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians](https://mlsanity.com/paper/2403.14166/) (2024)
- [HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression](https://mlsanity.com/paper/2403.14530/) (2024)
- [Taming 3DGS: High-Quality Radiance Fields with Limited Resources](https://mlsanity.com/paper/2406.15643/) (2024)
- [GaussianPro: 3D Gaussian Splatting with Progressive Propagation](https://mlsanity.com/paper/2402.14650/) (2024)
- [EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS](https://mlsanity.com/paper/2312.04564/) (2023)
- [Point-Based Neural Rendering with Per-View Optimization](https://mlsanity.com/paper/2109.02369/) (2021)
- [NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)](https://mlsanity.com/paper/2210.00379/) (2022)
- [Compact 3D Scene Representation via Self-Organizing Gaussian Grids](https://mlsanity.com/paper/2312.13299/) (2023)
- [Reducing the Memory Footprint of 3D Gaussian Splatting](https://mlsanity.com/paper/2406.17074/) (2024)
- [CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization](https://mlsanity.com/paper/2311.18159/) (2023)
- [RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS](https://mlsanity.com/paper/2403.13806/) (2024)
- [End-to-End Rate-Distortion Optimized 3D Gaussian Representation](https://mlsanity.com/paper/2406.01597/) (2024)
- [MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation](https://mlsanity.com/paper/2409.09756/) (2024)
- [LP-3DGS: Learning to Prune 3D Gaussian Splatting](https://mlsanity.com/paper/2405.18784/) (2024)
- [Spectrally Pruned Gaussian Fields with Neural Compensation](https://mlsanity.com/paper/2405.00676/) (2024)
- [AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field](https://mlsanity.com/paper/2405.12369/) (2024)
- [On the Computational Intractability of Exact and Approximate Dictionary Learning](https://mlsanity.com/paper/1405.6664/) (2014)

## Built on by (most cited first, 29 of 29)

- [Optimized Minimal 3D Gaussian Splatting](https://mlsanity.com/paper/2503.16924/) (2025)
- [Efficient Scene Modeling via Structure-Aware and Region-Prioritized 3D Gaussians](https://mlsanity.com/paper/2411.12788/) (2024)
- [Mobile-GS: Real-time Gaussian Splatting for Mobile Devices](https://mlsanity.com/paper/2603.11531/) (2026)
- [MEGS2: Memory-Efficient Gaussian Splatting via Spherical Gaussians and Unified Pruning](https://mlsanity.com/paper/2509.07021/) (2025)
- [ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction With Fewer Primitives](https://mlsanity.com/paper/2511.06810/) (2025)
- [SaLon3R: Structure-aware Long-term Generalizable 3D Reconstruction from Unposed Images](https://mlsanity.com/paper/2510.15072/) (2025)
- [POTR: Post-Training 3DGS Compression](https://mlsanity.com/paper/2601.14821/) (2026)
- [ControlGS: Consistent Structural Compression Control for Deployment-Aware Gaussian Splatting](https://mlsanity.com/paper/2505.10473/) (2025)
- [Optimizing 3D Gaussian Splattering for Mobile GPUs](https://mlsanity.com/paper/2511.16298/) (2025)
- [NanoGS: Training-Free Gaussian Splat Simplification](https://mlsanity.com/paper/2603.16103/) (2026)
- [Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting](https://mlsanity.com/paper/2606.30017/) (2026)
- [Provable Pruning for Efficient 3D Gaussian Splatting via Coresets](https://mlsanity.com/paper/2607.02721/) (2026)
- [GSta: Efficient Training Scheme with Siestaed Gaussians for Monocular 3D Scene Reconstruction](https://mlsanity.com/paper/2504.06716/) (2025)
- [Smol-GS: Compact Representations for Abstract 3D Gaussian Splatting](https://mlsanity.com/paper/2512.00850/) (2025)
- [Sketch&Patch++: Efficient Structure-Aware 3D Gaussian Representation](https://mlsanity.com/paper/2601.05394/) (2026)
- [Hybrid Latents: Geometry-Appearance-Aware Surfel Splatting](https://mlsanity.com/paper/2604.14928/) (2026)
- [P-4DGS: Predictive 4D Gaussian Splatting with 90× Compression](https://mlsanity.com/paper/2510.10030/) (2025)
- [SCAR-GS: Spatial Context Attention for Residuals in Progressive Gaussian Splatting](https://mlsanity.com/paper/2601.04348/) (2026)
- [SkipGS: Post-Densification Backward Skipping for Efficient 3DGS Training](https://mlsanity.com/paper/2603.08997/) (2026)
- [Compact Keyframe-Optimized Multi-Agent Gaussian Splatting SLAM](https://mlsanity.com/paper/2604.00804/) (2026)
- [MesonGS++: Post-training Compression of 3D Gaussian Splatting with Hyperparameter Searching](https://mlsanity.com/paper/2604.26799/) (2026)
- [DSD-GS: Dynamic-Static Decomposition of Gaussian Splatting for Efficient and High-Fidelity Dynamic Scene Reconstruction](https://mlsanity.com/paper/2605.30863/) (2026)
- [Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting](https://mlsanity.com/paper/2606.11782/) (2026)
- [Bake It Till You Make It: Ultrafast Spatial Texture-Atlas Splatting](https://mlsanity.com/paper/2607.13808/) (2026)
- [KISS-GS: 3D Gaussian Splatting Compression Kept Simple](https://mlsanity.com/paper/2608.26948/) (2026)
- [Towards Practical Compression of 3D Gaussian Splatting](https://mlsanity.com/paper/2609.30245/) (2026)
- [Mobile-4DGS: Unified Static-Dynamic Real-time Mobile Gaussian Splatting](https://mlsanity.com/paper/2610.05289/) (2026)
- [SplatQ: Interaction-Aware Pruning of 3D Gaussian Splatting via Quadratic Selection](https://mlsanity.com/paper/2610.13503/) (2026)
- [Efficient Confidence-Aware Densification for 3DGS](https://mlsanity.com/paper/2610.13541/) (2026)

## Abstract (arXiv)

3D Gaussian Splatting (3DGS) has emerged as a mainstream for novel view synthesis, leveraging continuous aggregations of Gaussian functions to model scene geometry. However, 3DGS suffers from substantial memory requirements to store the multitude of Gaussians, hindering its practicality. To address this challenge, we introduce GaussianSpa, an optimization-based simplification framework for compact and high-quality 3DGS. Specifically, we formulate the simplification as an optimization problem associated with the 3DGS training. Correspondingly, we propose an efficient "optimizing-sparsifying" solution that alternately solves two independent sub-problems, gradually imposing strong sparsity onto the Gaussians in the training process. Our comprehensive evaluations on various datasets show the superiority of GaussianSpa over existing state-of-the-art approaches. Notably, GaussianSpa achieves an average PSNR improvement of 0.9 dB on the real-world Deep Blending dataset with 10× fewer Gaussians compared to the vanilla 3DGS. Our project page is available at https://noodle-lab.github.io/gaussianspa/.