TRIDENT is a Blender add-on for generating 3D visualizations from dimensionality-reduced data such as UMAP, t-SNE, and PCA. It uses the EEVEE rendering engine for real-time navigation, category-based color labeling, and export of publication-grade images or animation frames.
TRIDENT transforms Blender into a scientific visualization environment. You can import embeddings from CSV files, render them as point clouds, assign category labels, and capture high-quality figures or animations directly within Blender’s interface.
TRIDENT reads CSV files where each row represents a data point in reduced-dimensional space.
Supports:
- XYZ coordinates (UMAP, t-SNE, PCA, etc.)
- Optional metadata columns for labels or grouping
After import, TRIDENT plots your data directly in the Blender viewport using the EEVEE renderer.
You can:
- Navigate freely through data clusters
- Toggle categories or classes on and off
- Adjust transparency and palettes
TRIDENT turns Blender into a figure-generation tool.
You can:
- Export publication-ready stills
- Render camera flythroughs
- Reuse lighting and material presets
To install TRIDENT, first download the release that matches your system directly from inside Blender from Edit → Preferences → Get Extensions, then search for TRIDENT. Or from the link below:
Blender Extensions: Download TRIDENT
Alternatively, it can be downloaded from here following the instructions. Github Releases: Download TRIDENT (Not Recommended)
Open Blender and go to Edit → Preferences → Get Extensions, then click Install from disk... from the menu on the top right arrow. Select the downloaded .zip file. After the add-on is installed, simply enable it by checking the box next to TRIDENT in the add-ons list.
Once installed, TRIDENT will be available in Blender’s sidebar (Press "N" to pop the sidebar).
For usage instructions and further tips, refer to the Wiki page.
TRIDENT requires Blender version 4.2 or higher. All necessary Python dependencies are bundled with the add-on, so no extra installation steps are needed.
Planned additions:
- Improve label handling and legend sorting
- Add support for point clouds and multiple plots across scenes
- Enable cluster management with options for merging, separation, and color-based visualization
- Introduce color animation, text, and image annotations within clusters
- Enhance usability with UI and UX refinements
This project is distributed under the GPL License.
