LoG utilizes a single RTX 4090 for training highly realistic urban-scale models and for their real-time rendering. Visit our project page for more demos.
log_demo0.mp4
Our code is built upon PyTorch and leverages gaussian-splatting techniques.
For a smooth setup, follow the installation guide.
We employ Colmap to prepare the dataset. Refer to the preprocessing documentation for detailed instructions. A minimal example dataset is provided here.
Training the model is as simple as one command:
python3 apps/train.py --cfg config/example/test/train.yml split trainWe automatically configure heuristic parameters based on the dataset size.
We provide a path for interpolation visualization
python3 apps/train.py --cfg config/example/test/train.yml split demo_interpolate ckptname output/example/test/level_of_gaussian/model_init.pthThe visualization video will be stored at output/example/test/level_of_gaussian/demo_interpolate/rgb.mp4
We will update a real-time rendering tool designed for immersive visualization.
We acknowledge the following inspirational prior work:
The rendering GUI is powered by our EasyVolcap tool.
Contributions are warmly welcomed! If you've made significant progress on any of these fronts, please consider submitting a pull request.
If you find this code useful for your research, please cite us using the following BibTeX entry.
@inproceedings{shuai2024LoG,
title={Real-Time View Synthesis for Large Scenes with Millions of Square Meters},
author={Shuai, Qing and Guo, Haoyu and Xu, Zhen and Lin, Haotong and Peng, Sida and Bao, Hujun and Zhou, Xiaowei},
year={2024}
}