# Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication

arXiv: https://arxiv.org/abs/2510.22718 · HTML: https://mlsanity.com/paper/2510.22718/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020)
- [A Survey of Recent Advances in Optimization Methods for Wireless Communications](https://mlsanity.com/paper/2401.12025/) (2024)
- [Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects](https://mlsanity.com/paper/2412.06257/) (2024)
- [Machine Intelligence at the Edge with Learning Centric Power Allocation](https://mlsanity.com/paper/1911.04922/) (2019)
- [CoSurfGS:Collaborative 3D Surface Gaussian Splatting with Distributed Learning for Large Scene Reconstruction](https://mlsanity.com/paper/2412.17612/) (2024)
- [Edge Accelerated Robot Navigation With Collaborative Motion Planning](https://mlsanity.com/paper/2311.08983/) (2023)
- [Communication Efficient Robotic Mixed Reality with Gaussian Splatting Cross-Layer Optimization](https://mlsanity.com/paper/2508.08624/) (2025)
- [An Efficient Sum-Rate Maximization Algorithm for Fluid Antenna-Assisted ISAC System](https://mlsanity.com/paper/2405.06516/) (2024)
- [Embodied Edge Intelligence Meets Near Field Communication: Concept, Design, and Verification](https://mlsanity.com/paper/2508.11232/) (2025)

## Built on by (most cited first, 2 of 2)

- [Improving Continual Learning for Gaussian Splatting based Environments Reconstruction on Commercial Off-the-Shelf Edge Devices](https://mlsanity.com/paper/2603.08499/) (2026)
- [Efficient Transceiver Design for Aerial Image Transmission and Large-scale Scene Reconstruction](https://mlsanity.com/paper/2604.11098/) (2026)

## Abstract (arXiv)

Gaussian splatting (GS) struggles with degraded rendering quality on low-cost devices. To address this issue, we present edge collaborative GS (ECO-GS), where each user can switch between a local small GS model to guarantee timeliness and a remote large GS model to guarantee fidelity. However, deciding how to engage the large GS model is nontrivial, due to the interdependency between rendering requirements and resource conditions. To this end, we propose integrated rendering and communication (IRAC), which jointly optimizes collaboration status (i.e., deciding whether to engage large GS) and edge power allocation (i.e., enabling remote rendering) under communication constraints across different users by minimizing a newly-derived GS switching function. Despite the nonconvexity of the problem, we propose an efficient penalty majorization minimization (PMM) algorithm to obtain the critical point solution. Furthermore, we develop an imitation learning optimization (ILO) algorithm, which reduces the computational time by over 100x compared to PMM. Experiments demonstrate the superiority of PMM and the real-time execution capability of ILO.