# MMGS: 10× Compressed 3DGS through Optimal Transport Aggregation based on Multi-view Ranking

arXiv: https://arxiv.org/abs/2605.19304 · HTML: https://mlsanity.com/paper/2605.19304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 19 May 2026.

## Builds on (most cited first, 6 of 6)

- [Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians](https://mlsanity.com/paper/2403.17898/) (2024)
- [Trimming the Fat: Efficient Compression of 3D Gaussian Splats through Pruning](https://mlsanity.com/paper/2406.18214/) (2024)
- [FastGS: Training 3D Gaussian Splatting in 100 Seconds](https://mlsanity.com/paper/2511.04283/) (2025)
- [Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields](https://mlsanity.com/paper/2408.03822/) (2024)
- [AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field](https://mlsanity.com/paper/2405.12369/) (2024)
- [Gaussian Herding across Pens: An Optimal Transport Perspective on Global Gaussian Reduction for 3DGS](https://mlsanity.com/paper/2506.09534/) (2025)

## Built on by (most cited first, 1 of 1)

- [Robust and Efficient Monocular 3D Gaussian SLAM for Kilometer-Scale Outdoor Scenes](https://mlsanity.com/paper/2606.30436/) (2026)

## Abstract (arXiv)

While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction, it suffers from significant overhead due to massive redundant primitives. Existing compression methods typically rely on local sampling or fixed pruning thresholds, which often struggle to balance redundancy reduction with high-fidelity rendering. To address this, we propose a novel framework that formulates Gaussian optimization as a global geometric distribution matching problem. Specifically, our approach integrates three components: (1) we introduce a multi-view 3D Gaussian contribution ranking mechanism that filters primitives using geometric consistency instead of local heuristics; (2) we propose a global Optimal Transport (OT)-based aggregation algorithm that merges redundant primitives while preserving the underlying geometry; and (3) we design an OT-based densification operator that maintains the Gaussian's distributional properties for stable optimization. Our approach achieves state-of-the-art rendering quality with only 10% primitives and 10× accelerated training speeds compared to vanilla 3DGS.