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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.
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.