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

💾 Installation

pip install vedo

additional installation details [click to expand]

• 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

📙 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 a large set of working examples for a wide range of functionalities

working with polygonal meshes and point clouds [click to expand]

• 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.

working with volumetric data and tetrahedral meshes

• 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)

plotting and histogramming in 2D and 3D

• 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 other libraries

• Integration with the Qt5 framework.
• Support for FEniCS/Dolfin platform for visualization of PDE/FEM solutions.
• 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

volumetric files (slc, tiff, DICOM...) can be visualized in different modes [click to expand]

Volume 3D slicing vedo --slicer embryo.slc	Ray-casting vedo -g	2D slicing vedo --slicer2d

Type vedo -h for the complete list of options.

🐾 Gallery

vedo currently includes 300+ 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 that would make this better, please fork the repo and create a pull request. This is how:

# 1. Fork the repository on GitHub then clone your fork locally:
git clone https://github.com/your-username/vedo.git
# 2. Create a new branch for your feature or bugfix:
git checkout -b feature/my-feature
# 3. Make your changes and commit them:
git commit -m "Description of my feature"
# 4. Push your changes to your fork:
git push origin feature/my-feature
# 5. Open a Pull Request on the main repository.

You can also simply open an issue with the tag "enhancement".

📜 References

Scientific publications leveraging vedo:

• X. Diego et al.: "Key features of Turing systems are determined purely by network topology", Phys. Rev. X 8, 021071, DOI.
• M. Musy, K. Flaherty et al.: "A Quantitative Method for Staging Mouse Limb Embryos based on Limb Morphometry", Development (2018) 145 (7): dev154856, DOI.
• F. Claudi, A. L. Tyson, T. Branco, "Brainrender. A python based software for visualisation of neuroanatomical and morphological data.", eLife 2021;10:e65751, DOI.
• J. S. Bennett, D. Sijacki, "Resolving shocks and filaments in galaxy formation simulations: effects on gas properties and star formation in the circumgalactic medium", Monthly Notices of the Royal Astronomical Society, Volume 499, Issue 1, DOI.
• J.D.P. Deshapriya et al., "Spectral analysis of craters on (101955) Bennu". Icarus 2020, DOI.
• A. Pollack et al., "Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study", Geothermics, Volume 95, September 2021, DOI.
• X. Lu et al., "3D electromagnetic modeling of graphitic faults in the Athabasca Basin using a finite-volume time-domain approach with unstructured grids", Geophysics, DOI.
• M. Deepa Maheshvare et al., "A Graph-Based Framework for Multiscale Modeling of Physiological Transport", Front. Netw. Physiol. 1:802881, DOI.
• F. Claudi, T. Branco, "Differential geometry methods for constructing manifold-targeted recurrent neural networks", bioRxiv 2021.10.07.463479, DOI.
• J. Klatzow, G. Dalmasso, N. Martínez-Abadías, J. Sharpe, V. Uhlmann, "µMatch: 3D shape correspondence for microscopy data", Front. Comput. Sci., 15 February 2022. DOI
• G. Dalmasso et al., "4D reconstruction of murine developmental trajectories using spherical harmonics", Developmental Cell 57, 1–11 September 2022, DOI.
• D.J.E Waibel et al., "Capturing Shape Information with Multi-scale Topological Loss Terms for 3D Reconstruction", Lecture Notes in Computer Science, vol 13434. Springer, Cham. DOI.
• N. Lamb et al., "DeepJoin: Learning a Joint Occupancy, Signed Distance, and Normal Field Function for Shape Repair", ACM Transactions on Graphics (TOG), vol 41, 6, 2022. DOI
• J. Cotterell et al., "Cell 3D Positioning by Optical encoding (C3PO) and its application to spatial transcriptomics", bioRxiv 2024.03.12.584578; DOI

Have you found this software useful for your research? Star ✨ the project and cite it as:

M. Musy et al., "vedo, a python module for scientific analysis and visualization of 3D objects and point clouds", Zenodo, 2021, doi: 10.5281/zenodo.7019968.