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High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification and Regularized Optimization
- Python 98.4%
- Jupyter Notebook 1%
- Shell 0.6%
| bash_scripts | ||
| configs | ||
| datasets | ||
| exp_code | ||
| external | ||
| scripts | ||
| third_party/diff_rasterization_w_d | ||
| utils | ||
| viz_scripts | ||
| .gitignore | ||
| README.md | ||
| requirements.txt | ||
RGB-D Gaussian Splatting SLAM
Installation
Prerequisites
- Python 3.10
- CUDA 11.8 or compatible version
- uv package manager
Environment Setup with uv
Quick Install (Recommended)
Create a virtual environment and install all dependencies:
# Create virtual environment with Python 3.10
uv venv --python 3.10
Activate the virtual environment
source .venv/bin/activate
Install all dependencies
uv pip install -r requirements.txt
Install Gaussian Rasterization Package
After installing the base dependencies, install the custom Gaussian rasterization package:
uv pip install submodules/diff_rasterization_w_d --no-build-isolation
uv pip install git+ssh://git@github.com/cvg/LightGlue.git@b1cd942