Member-only story

Hands-on Tutorials, 3D Python

How to automate LiDAR point cloud sub-sampling with 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.

Florent Poux, Ph.D.
Towards Data Science
13 min readNov 21, 2020

--

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

In this article, I will give you my two favourite 3D processes for quickly structuring and sub-sampling point cloud data with python. You will also be able to automate, export, visualize and integrate results into your favourite 3D software, without any coding experience. I will focus on code optimization while using a minimum number of libraries (mainly NumPy) so that you can extend what you learnt with very high flexibility! Ready 😁?

Why do we need to sub-sample point clouds?

Point cloud datasets are marvellous! You can get a geometric description of world entities by discretizing them through a bunch of points, which, aggregated together, resemble the shape — the environment — of interest.

--

--

Towards Data Science
Towards Data Science

Published in Towards Data Science

Your home for data science and AI. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.

Florent Poux, Ph.D.
Florent Poux, Ph.D.

Written by Florent Poux, Ph.D.

🏆 Director of Science | 3D Data + Spatial AI. https://learngeodata.eu (💻 + 📦 + 📙 + ▶️)

Responses (9)