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3D Geodata Ad. Professor @ULiège | Data Sciences & ML Mentor @OpenClassroom | Award-winning researcher & Engineer | Formation at: https://learngeodata.eu

3D Python

Tutorial for advanced visualization and interaction with big point cloud data in Python. (Bonus) Learn how to create an interactive segmentation “software”.

The Drone 3D Point Cloud processed and visualised in this article. You will learn feature extraction, interactive and automatic segmentation while visualising in real-time and creating animations. © F. Poux


Hands-on Tutorials, 3D Python

The ultimate guide to subsample 3D point clouds from scratch, with Python. Two efficient methods are shown to import, process, structure as a voxel grid, and visualise LiDAR data.

Point cloud sampling results by following the strategies explained in this guide. © F. Poux

Why do we need to sub-sample point clouds?


3D Geodata

Different clustering strategies applied to this noisy point cloud of a room. One can see that spatial proximities seems a choice criterion to define this similarity to constitute segments. © Florent Poux, Ph.D.

What is clustering?


Scale AI releases a new open-source dataset for both academic and commercial use, and accelerate the growth of Autonomous Driving research.

Example of a data labeling guided process with semi-automatic image labeling. Image credits: segments.ai


3D Ideas

Different data representation of a 3D point cloud dataset

3D Point Clouds


3D Python

Tutorial to generate 3D meshes (.obj, .ply, .stl, .gltf) automatically from 3D point clouds using python. (Bonus) Surface reconstruction to create several Levels of Detail.

Several meshes automatically generated using Python. At the end of this article, you will be able to create your datasets from point clouds


3D Python

Tutorial to simply set up your python environment, start processing and visualize 3D point cloud data. Highlights Anaconda, NumPy, Matplotlib and Google Colab.

5-Step Guide to set-up your python environment

  • We need to set-up our environment. I recommend to download Anaconda Navigator, which comes with an easy GUI.
  • Once downloaded and installed, create an environment (2nd tab on the left > Create button), which allows you to specify a python version (the latest is fine). You know it is selected when the green arrow is next to it.


Discrete spatial datasets known as point clouds often lay the groundwork for decision-making applications. But can they become the next big thing?

Different renderings of a point cloud. From left to right, raw point cloud, shaded, colored, voxelized, semantized

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