RegGS

<p align="center"> <h2 align="center">RegGS: Unposed Sparse Views Gaussian Splatting with 3DGS Registration</h2> <p align="center"> <strong>Chong Cheng</strong>^1* · <strong>Yu Hu</strong>^1* · <strong>Sicheng Yu</strong>¹ · <strong>Beizhen Zhao</strong>¹ · <strong>Zijian Wang</strong>¹ · <strong>Hao Wang</strong>^1† </p> <p align="center"><strong>International Conference on Computer Vision, ICCV 2025</strong></a> <p align="center"> ¹The Hong Kong University of Science and Technology (Guangzhou) </p> <h3 align="center">

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🛠️ Setup

The code has been tested on systems with:

• Ubuntu 22.04 LTS
• Python 3.10.18
• CUDA 11.8
• NVIDIA GeForce RTX 3090 or A6000

📦 Repository

Clone the repo with --recursive because we have submodules:

git clone https://github.com/3DAgentWorld/RegGS.git --recursive
cd RegGS

💻 Installation

Python Environment

This codebase has been successfully tested with Python 3.10, CUDA 11.8, and PyTorch 2.5.1. We recommend installing the dependencies in a virtual environment such as Anaconda.

1. Install main libraries:

   conda env create -f environment.yaml
   
   conda activate reggs
   
   pip install -r requirements.txt
   

2. Install thirdparty submodules:

   pip install thirdparty/diff-gaussian-rasterization-w-pose
   
   pip install thirdparty/gaussian_rasterizer`
   
   pip install thirdparty/simple-knn
   

3. Compile the cuda kernels for RoPE (as in CroCo v2):

   cd src/noposplat/model/encoder/backbone/croco/curope
   python setup.py build_ext --inplace
   

4. If you encounter cannot import torch. add option --no-build-isolation to pip install

Downloading Pretrained Checkpoints

Download NoPoSplat re10k checkpoints and acid checkpoints to ./pretrained_weights directory, run:

wget -c https://huggingface.co/botaoye/NoPoSplat/resolve/main/re10k.ckpt -P ./pretrained_weights

wget -c https://huggingface.co/botaoye/NoPoSplat/resolve/main/acid.ckpt -P ./pretrained_weights

Run RegGS on re10k sample

The preprocessed re10k data is placed in the directory ./sample_data. To run RegGS on sample data, run:

1. The inference stage:
   CUDA_VISIBLE_DEVICES=0 python3 run_infer.py config/re10k.yaml
2. The refinement stage:
   CUDA_VISIBLE_DEVICES=0 python3 run_refine.py --checkpoint_path output/re10k/000c3ab189999a83
3. The evaluation stage:
   CUDA_VISIBLE_DEVICES=0 python3 run_metric.py --checkpoint_path output/re10k/000c3ab189999a83

✏️ TODO

• [x] create codebase
• [x] add evaluation script
• [x] prepare sample data
• [x] write installation guide
• [ ] add data preprocessing script
• [ ] implement GPU-optimized k-means
• [ ] add Gradio visualization

🎓 Citation

@inproceedings{cc2025_reggs,
  title     = {{RegGS}: Unposed Sparse Views Gaussian Splatting with {3DGS} Registration},
  author    = {Cheng, Chong and Hu, Yu and Yu, Sicheng and Zhao, Beizhen and Wang, Zijian and Wang, Hao},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      = {2025}
}