Tutorials for pointcloud processing in Python

The use of pointclouds tends to increase over the years, as 3D acquisition systems and 3D modeling software become more widely available. Pointclouds are nowadays used in many areas, such as computer-aided design, metrology, extended reality, robotics, and autonomous driving, to name just a few.

These tutorials are for those wishing to learn a little bit more about the basics of pointcloud processing. Having gone through this stage during my Ph.D., I hope here to share some of what I have learned so far.

The notebooks are designed to make pointcloud processing algorithms easier to understand, without compromising performance too much and trying to minimize the use of specialized third-party software. They require basic knowledge of Python and its main scientific libraries.

Content

Tutorials are broken down as follows:

0. Basics: structure and attributes.
1. Spatial transformations: translations, rotations, reflections & scaling.
2. Spatial indexing: voxel grids, octrees & kd-trees.
3. Registration: corresponding sets & non-corresponding sets (Iterative Closest Point & Principal Axis Alignment).
4. Segmentation: Region Growing, Hough Transform & RANSAC.
5. Primitive fitting: plane, sphere, cylinder, cone & torus.
6. Normals and descriptors: normals, curvatures and 2D/3D features.

To do list:

• Machine learning
• Deep learning
• Subsampling

Dependencies

The code is in python and relies on numpy, scipy, matplotlib, and jupyterlab.

These dependencies may be installed with pip with

pip install numpy scipy matplotlib jupyterlab

or via conda with

conda install numpy scipy matplotlib jupyterlab

JupyterLab may be started using the terminal or Anaconda prompt simply by typing

jupyter lab

Going further

The lists below do not pretend to be not exhaustive but may be a good starting point for those who whish to dive deeper in the topic of pointcloud processing with Python.

Software

Libraries (in alphabetical order):

• CGAL, an open-source library for efficient and reliable geometric algorithms (in C++, with Python bidings)
• CloudComPy, a Python wrapper for CloudCompare (see below)
• Open3D, an open-source library for 3D data processing (in C++ and Python, with a 3D viewer app)
• PCL, a standalone, large scale, open project for 2D/3D image and pointcloud processing (in C++, with Python bidings)
• PDAL, an open-source library for translating and manipulating pointcloud data (in C++, with Python support)
• PyMeshLab, a Python library that interfaces to MeshLab (see below)
• PyVista, a library providing a pythonic interface to VTK (see below)
• VTK, an open-source software for manipulating and displaying scientific data (in C++, with wrappers in Python, Java and Tcl)

Applications (in alphabetical order):

• Blender, an open-source 3D computer graphics software that may be used to visualize and process pointclouds (with Python scripting capabilities)
• CloudCompare, an open-source 3D pointcloud (and triangular mesh) processing software (with Python scripting capabilities through CloudComPy)
• MeshLab, an open-source 3D triangular meshes (and pointclouds) processing and editing software (with Python scripting capabilities through PyMeshLab)
• ParaView, an open-source visualization application (with Python scripting capabilities)

Resources

Books (in reverse chronological order):

• Poux, F. (2025). 3D Data Science with Python. O'Reilly Media.
• Liu, S., Zhang, M., Kadam, P., & Kuo, C. C. J. (2021). 3D Point Cloud Analysis: Traditional, Deep Learning, and Explainable Machine Learning Methods. Springer.
• Vosselman, G., & Maas, H. G. (2010). Airborne and terrestrial laser scanning. Whittles Publishing.
• Samet, H. (2006). Foundations of multidimensional and metric data structures. Morgan Kaufmann.
• Schneider, P., & Eberly, D. H. (2002). Geometric tools for computer graphics. Elsevier.
• Goulette, F. (1999). Modélisation 3D automatique : outils de géométrie différentielle. Presses des Mines.

Videos (in alphabetical order):

• CloudCompare playlist on Daniel Girardeau-Montaut YouTube channel www.youtube.com/@danielgirardeau-montaut9044 (last accessed in October 2025)
• Florent Poux YouTube channel: www.youtube.com/@FlorentPoux (last accessed in October 2025)

Reusing and distributing

You are free to share and adapt the content of these notebooks as long as you give appropriate credit and do not use it for commercial purposes. You are free to use, modify, and distribute the code contained in these notebooks as you wish. You can view the full license here.

An example of citation using BibTeX:

@unpublished{gregorio2024tutorials,
     author={Grégorio, Jean-Loup},
     title={Tutorials for pointcloud processing in Python},
     year={2024},
}