The modular workstation for 3D Gaussian Splatting
Train, inspect, edit, automate, and export 3D Gaussian Splatting scenes from a single native application.
LichtFeld Studio lets you train new scenes from COLMAP datasets, resume checkpoints, inspect reconstructions in real time, edit gaussian selections, extend the app with Python plugins, and automate workflows through MCP and embedded Python.
Download Windows (Portal) •
Build From Source •
Python API and Plugins •
MCP Guide •
Support Development •
Join Discord
See It in Action •
Why LichtFeld •
Who It Is For •
Capabilities •
Installation •
Docs •
Community •
Contributing •
License
See It in Action
▶ Watch on YouTube — Everything LichtFeld Studio can do today
Why LichtFeld Studio
LichtFeld Studio is built for users who need more than a training script or a standalone viewer. It combines model training, real-time visualization, gaussian editing, export, plugins, and automation in one toolchain.
- Train new 3D Gaussian Splatting scenes and continue experiments from checkpoints
- Inspect reconstructions interactively while training or after convergence
- Select, transform, and edit gaussian subsets and scene nodes with undo/redo support
- Export results to
PLY, SOG, SPZ, or a standalone HTML viewer
- Extend the application with Python plugins and plugin-local dependencies
- Automate workflows through MCP resources, MCP tools, and embedded Python
Who It Is For
- Researchers: iterate on reconstruction quality, inspect training progress, test advanced features, and export results for analysis or sharing
- Production teams: inspect scenes visually, edit gaussian selections, and deliver portable exports without stitching together separate tools
- Tool builders: integrate LichtFeld Studio into larger pipelines through plugins, embedded Python, and MCP-driven automation
Capabilities
- Training and iteration: load datasets, resume checkpoints, monitor progress, and evaluate changes in a desktop app or headless workflow
- Interactive scene work: inspect reconstructions in real time, work with gaussian selections, and apply scene transforms with history support
- Export and delivery: export results to common research and delivery formats, including a standalone HTML viewer for easy sharing
- Extensibility: use the Python plugin system for custom panels, operators, tools, and dependencies
- Automation surface: integrate LichtFeld Studio with local tools, scripts, and agents through MCP resources and tools
- Research-ready features: MCMC optimization, bilateral grid appearance modeling, 3DGUT support for distorted camera models, and timelapse generation
- Native performance: modern C++23 and CUDA 12.8+ for responsive training and visualization on NVIDIA hardware
Optional POPSpa sparsification
In the Training panel, enable Sparsity and set Target Gaussians under
Sparsity Settings. The CLI equivalent is:
--enable-sparsity --popspa-target-count 256000
POPSpa runs after regular training: contribution scoring, an intermediate prune,
sparse optimization, an exact final prune, and recovery. It supports FastGS and
the bundled gsplat/GUT backend. The target must fit the active model count and
retain all frozen Gaussians. Phase lengths and advanced controls are available as
popspa_* settings in the UI, JSON, and Python.
The equations follow KissGsVanilla (Apache-2.0), implemented independently in LichtFeld Studio's C++/CUDA.
Support Development
LichtFeld Studio is free and open source. If it is useful in your research, production, or learning workflow, please consider supporting its continued development.
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