# GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds

arXiv: https://arxiv.org/abs/2606.05650 · HTML: https://mlsanity.com/paper/2606.05650/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 4 Jun 2026.

## Builds on (most cited first, 15 of 15)

- [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)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [Panoptic Studio: A Massively Multiview System for Social Interaction Capture](https://mlsanity.com/paper/1612.03153/) (2016)
- [CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization](https://mlsanity.com/paper/2311.18159/) (2023)
- [MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation](https://mlsanity.com/paper/2409.09756/) (2024)
- [Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields](https://mlsanity.com/paper/2408.03822/) (2024)
- [GPS-Gaussian+: Generalizable Pixel-wise 3D Gaussian Splatting for Real-Time Human-Scene Rendering from Sparse Views](https://mlsanity.com/paper/2411.11363/) (2024)
- [4DGCPro: Efficient Hierarchical 4D Gaussian Compression for Progressive Volumetric Video Streaming](https://mlsanity.com/paper/2509.17513/) (2025)
- [Perceptual Video Quality Prediction Emphasizing Chroma Distortions](https://mlsanity.com/paper/2009.11203/) (2020)
- [Adaptive Voxelization for Transform coding of 3D Gaussian splatting data](https://mlsanity.com/paper/2506.00271/) (2025)
- [Dynamic Polygon Clouds: Representation and Compression for VR/AR](https://mlsanity.com/paper/1610.00402/) (2016)
- [CompGS++: Compressed Gaussian Splatting for Static and Dynamic Scene Representation](https://mlsanity.com/paper/2504.13022/) (2025)
- [A Study of the Fundamental Performance Characteristics of GPUs and CPUs for Database Analytics (Extended Version)](https://mlsanity.com/paper/2003.01178/) (2020)

## Abstract (arXiv)

Dynamic 3D Gaussian Splatting (3DGS) holds great promise as a 3D video streaming technology since it can represent complex 3D scenes with high fidelity. In this approach, every frame in a 3D video represents the environment as a collection of Gaussians with position and other attributes such as scale, rotation, opacity, and color. Frames capture fine details, permit views from any arbitrary perspective, but are an order of magnitude, or more, larger than 2D video frames. A line of recent work has explored how to compress dynamic 3DGS frames, but these approaches are often slow, in part because their compression techniques are not amenable to efficient acceleration. GS-NFS accelerates dynamic 3DGS compression and decompression on a GPU, to the point where it can encode and decode at full frame rate. It achieves this by developing novel GPU-based parallelizations of existing algorithms for encoding both positions and attributes of Gaussians. As a result, it is 1-2 orders of magnitude faster than the state-of-the-art in encoding and decoding a frame, while offering competitive compression performance and rendering quality.