Mobile-GS: Real-time Gaussian Splatting for Mobile Devices

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😮 Highlights

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🚩 Updates

Welcome to watch 👀 this repository for the latest updates.

✅ [2026.3.13] : Release project page.

✅ [2026.3.13] : Code Release.

Setup

For installation: We recommend to use cuda 12.1 with python 3.11 for easy setup.

git clone git@github.com:xiaobiaodu/Mobile-GS.git

conda create -n mobile-gs python==3.11
conda activate mobile-gs

pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

Install TMC (GPCC), and add tmc3 to your environment variable or manually specify its location in the code (lines 243 and 258, this script is sourced from HAC++).

If you have trouble in installing cuml, please refer to the CUML Installation Guide.

We used Mip-NeRF 360, Tanks & Temples, and Deep Blending.

Running

Pre-training (Mini-Splatting)

#for outdoor scenes (e.g., Mip-NeRF 360 outdoor and T&T scenes)
python pretrain.py -s <path to COLMAP>  -m  <model path> --eval --imp_metric outdoor --sh_degree 3   --iterations 30000
#for indoor scenes (e.g., Mip-NeRF 360 indoor and DB scenes)
python pretrain.py -s <path to COLMAP>  -m  <model path> --eval --imp_metric indoor --sh_degree 3   --iterations 30000

Fine-tune

python train.py -s  <path to COLMAP> -m <model path>  --eval --start_checkpoint  <model path>/chkpnt30000.pth 

# To improve rendering perofmrance, you can use multi-view training from MVGS. It may cause longer training time and memory.
python train.py -s  <path to COLMAP> -m <model path>  --eval --start_checkpoint  <model path>/chkpnt30000.pth   --mv  3

Evaluation

python render.py -s <path to COLMAP> -m <model path> --decode
python metrics.py -m <model path> 

--decode

Rendering with the compressed file (comp.xz), otherwise using the ply file. The results are the same regardless of this option.

👍 Acknowledgement

This work is built on many amazing research works and open-source projects, thanks a lot to all the authors for sharing!

• Mini-Splatting
• OMG
• MVGS

BibTeX

@misc{du2026mobile-gs,
      title={Mobile-GS: Real-time Gaussian Splatting for Mobile Devices}, 
      author={Xiaobiao Du and Yida Wang and Kun Zhan and Xin Yu},
      year={2026},
      eprint={2603.11531},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.11531}, 
}