vedo

Your friendly python module for scientific analysis and visualization of 3d objects.

💾 Installation

pip install vedo

• To install the latest dev version of vedo:

pip install -U git+https://github.com/marcomusy/vedo.git

• To install from the conda-forge channel:

conda install -c conda-forge vedo

🚀 Quick Start

from vedo import Sphere, show

sphere = Sphere().c("tomato")
show(sphere, axes=1).close()

This opens an interactive 3D window with a simple object and axes.

📙 Documentation

The webpage of the library with documentation is available here.

📌 Need help? Have a question, or wish to ask for a missing feature? Do not hesitate to ask any questions on the image.sc forum or by opening a github issue.

🎨 Features

The library includes hundreds of working examples for a wide range of functionalities

• Import meshes from VTK format, STL, Wavefront OBJ, 3DS, Dolfin-XML, Neutral, GMSH, OFF, PCD (PointCloud),
• Export meshes as ASCII or binary to VTK, STL, OBJ, PLY ... formats.
• Analysis tools like Moving Least Squares, mesh morphing and more..
• Tools to visualize and edit meshes (cutting a mesh with another mesh, slicing, normalizing, moving vertex positions, etc..).
• Split mesh based on surface connectivity. Extract the largest connected area.
• Calculate areas, volumes, center of mass, average sizes etc.
• Calculate vertex and face normals, curvatures, feature edges. Fill mesh holes.
• Subdivide faces of a mesh, increasing the number of vertex points. Mesh simplification.
• Coloring and thresholding of meshes based on associated scalar or vectorial data.
• Point-surface operations: find nearest points, determine if a point lies inside or outside of a mesh.
• Create primitive shapes: spheres, arrows, cubes, torus, ellipsoids...
• Generate glyphs (associate a mesh to every vertex of a source mesh).
• Create animations easily by just setting the position of the displayed objects in the 3D scene. Add trailing lines and shadows to moving objects is supported.
• Straightforward support for multiple sync-ed or independent renderers in the same window.
• Registration (alignment) of meshes with different techniques.
• Mesh smoothing.
• Delaunay triangulation in 2D and 3D.
• Generate meshes by joining nearby lines in space.
• Find the closest path from one point to another, traveling along the edges of a mesh.
• Find the intersection of a mesh with lines, planes or other meshes.
• Interpolate scalar and vectorial fields with Radial Basis Functions and Thin Plate Splines.
• Add sliders and buttons to interact with the scene and the individual objects.
• Visualization of tensors.
• Analysis of Point Clouds
• Moving Least Squares smoothing of 2D, 3D and 4D clouds
• Fit lines, planes, spheres and ellipsoids in space
• Identify outliers in a distribution of points
• Decimate a cloud to a uniform distribution.

• Import data from VTK format volumetric TIFF stacks, DICOM, SLC, MHD and more
• Import 2D images as PNG, JPEG, BMP
• Isosurfacing of volumes
• Composite and maximum projection volumetric rendering
• Generate volumetric signed-distance data from an input surface mesh
• Probe volumes with lines and planes
• Generate stream-lines and stream-tubes from vectorial fields
• Slice and crop volumes
• Support for other volumetric structures (structured and grid data)

• Polygonal 3D text rendering with Latex-like syntax and unicode characters, with 30 different fonts.
• Fully customizable axis styles
• donut plots and pie charts
• Scatter plots in 2D and 3D
• Surface function plotting
• 1D customizable histograms
• 2D hexagonal histograms
• Polar plots, spherical plots and histogramming
• Draw latex-formatted formulas in the rendering window.
• Quiver, violin, whisker and stream-line plots
• Graphical markers analogous to matplotlib

• Integration with the Qt5 framework.
• Interoperability with the trimesh, pyvista and pymeshlab libraries.
• Export 3D scenes and embed them into a web page.
• Embed 3D scenes in jupyter notebooks with K3D (can export an interactive 3D-snapshot page here).

⌨ Command Line Interface

Visualize a polygonal mesh or a volume from a terminal window simply with:

vedo https://vedo.embl.es/examples/data/embryo.tif

Volume 3D slicingvedo --slicer embryo.slc	Ray-castingvedo -g	2D slicingvedo --slicer2d

Type vedo -h for the complete list of options.

🐾 Gallery

vedo currently includes hundreds of working examples and notebooks.

Run any of the built-in examples. In a terminal type: vedo -r warp2

Check out the example galleries organized by subject here:

✏ Contributing

Any contributions are greatly appreciated. If you have a suggestion, bugfix, feature, or documentation improvement, please open an issue or submit a pull request.

See CONTRIBUTING.md for contribution guidelines and workflow details.

📜 References

Scientific publications leveraging vedo:

2026

• L. Aviñó-Esteban et al., "Limblab: pipeline for 3D analysis and visualisation of limb bud gene expression", BMC Bioinformatics 27(1): 6 (2026).
• D. Krsikapa, I. Y. Kim, "Gradient-based optimization of component layout: addressing accessibility and mounting in assembly system design", Journal of Mechanical Design 148(3): 031702 (2026).

2025

• A. Kharlamova et al., "Spatial CAPTCHA: Generatively Benchmarking Spatial Reasoning for Human-Machine Differentiation", arXiv preprint arXiv:2510.03863 (2025).
• J. F. Fuhrmann et al., "Apical extracellular matrix regulates fold morphogenesis in the Drosophila wing disc", bioRxiv 2025-09 (2025).
• B. Li et al., "Three-dimensional spatial transcriptomics at isotropic resolution enabled by generative deep learning", bioRxiv 2025-08 (2025).
• T.-T. Hsu et al., "Shared Alteration of Whole-Brain Connectivity and Olfactory Deficits in Multiple Autism Mouse Models", bioRxiv 2025-02 (2025).
• A. Arrabi et al., "C-arm guidance: A self-supervised approach to automated positioning during stroke thrombectomy", 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI).
• L. Aviñó-Esteban, H. Cardona-Blaya, J. Sharpe, "Spatio-temporal reconstruction of gene expression patterns in developing mice", Development 152: DEV204313 (2025), DOI.
• B. Bortolon et al., "GRASPLAT: Enabling dexterous grasping through novel view synthesis", 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
• L. Carreira et al., "Targeted nano-energetic material exploration through active learning algorithm implementation", Energetic Materials Frontiers 6(1): 3-13 (2025).
• M. Chirillo et al., "PyReconstruct: A fully open-source, collaborative successor to Reconstruct", Proceedings of the National Academy of Sciences 122(31): e2505822122 (2025).
• B. Clayton et al., "A facile method to create continuum stochastic sheet-based cellular materials", Additive Manufacturing: 104917 (2025).
• A. Gross et al., "STRESS, an automated geometrical characterization of deformable particles for in vivo measurements of cell and tissue mechanical stresses", Scientific Reports 15(1): 28599 (2025).
• A. Gauvain et al., "HydroModPy: A Python toolbox for deploying catchment-scale shallow groundwater models" (2025).
• K. N. Halwachs et al., "Effects of Stiffness and Degradability on Cardiac Fibroblast Contractility and Extracellular Matrix Secretion in Three-Dimensional Hydrogel Scaffolds", ACS Biomaterials Science & Engineering 11(11): 6521-6533 (2025).
• R. Kliman et al., "Toward an Automated System for Nondestructive Estimation of Plant Biomass", Plant Direct 9(3): e70043 (2025).

2024

• C. Lei et al., *"