gaussian_renderer 0.2.0

A Gaussian Splatting Renderer and Tools

Gaussian Renderer

A Gaussian Splatting Renderer and Tools package.

中文文档

This repository primarily provides rendering capabilities for Gaussian Splatting models. It is developed as a component of the DISCOVERSE project.

For detailed usage within the simulation environment, see: https://github.com/TATP-233/DISCOVERSE

Requirements

Python >= 3.10

Installation

uv add gaussian-renderer
# or: pip install gaussian-renderer

From source:

git clone https://github.com/TATP-233/GaussainRenderer.git
cd GaussainRenderer
uv pip install .
# or: pip install .

Optional extras

uv add "gaussian-renderer[viewer]"   # OpenGL viewer (glfw, PyOpenGL)
uv add "gaussian-renderer[mujoco]"   # MuJoCo integration
uv add "gaussian-renderer[motrix]"   # MotrixSim integration

# Combine as needed
uv add "gaussian-renderer[viewer,mujoco]"
# or: pip install ".[viewer,mujoco]"

Usage

Command-line tools

gs-viewer — OpenGL viewer for .ply models

gs-viewer path/to/model.ply

Controls: Left mouse = rotate, Right/Middle = pan, Scroll = zoom, Up/Down = SH degree, Drag & drop = load file

gs-compress — Compress 3DGS PLY to SuperSplat format

gs-compress input.ply
gs-compress input.ply -o output.ply
gs-compress models/          # batch

gs-transform — Apply translation/rotation/scale to a model

gs-transform input.ply -o output.ply -t 0 1 0 -s 2.0
gs-transform input.ply -r 0 0 0 1   # rotation quaternion xyzw
# --compress: save as compressed PLY

Python API

uv run python -m gaussian_renderer.simple_viewer path/to/model.ply
uv run python -m gaussian_renderer.supersplat_compress input.ply
uv run python -m gaussian_renderer.transform_gs_model input.ply

Development

uv pip install ".[dev]"
# or: pip install ".[dev]"
make lint       # ruff check
make format     # ruff format
make typecheck  # mypy
make test       # pytest
make ci         # all of the above

Citation

@article{jia2025discoverse,
      title={DISCOVERSE: Efficient Robot Simulation in Complex High-Fidelity Environments},
      author={Yufei Jia and Guangyu Wang and Yuhang Dong and Junzhe Wu and Yupei Zeng and Haonan Lin and Zifan Wang and Haizhou Ge and Weibin Gu and Chuxuan Li and Ziming Wang and Yunjie Cheng and Wei Sui and Ruqi Huang and Guyue Zhou},
      journal={arXiv preprint arXiv:2507.21981},
      year={2025},
      url={https://arxiv.org/abs/2507.21981}
}