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3D Point Cloud Shape Detection for Indoor Modelling

A 10-step Python Guide to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Space Occupancy 3D Modeling of Indoor Point Cloud Datasets.

Florent Poux, Ph.D.
Towards Data Science
28 min readSep 7, 2023

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If you have experience with point clouds or data analysis, you know how crucial it is to spot patterns. Recognizing data points with similar patterns, or "objects," is important to gain more valuable insights. Our visual cognitive system accomplishes this task easily, but replicating this human ability through computational methods is a significant challenge.

The goal is to utilize the natural tendency of the human visual system to group sets of elements. 👀

Example of a result of the Segmentation phase on the 3D Point Cloud. © F. Poux

But why is it useful?

First, it lets you easily access and work with specific parts of the data by grouping them into segments. Secondly, it makes the data processing faster by looking at regions instead of individual points. This can save a lot of time and energy. And finally, segmentation can help you find patterns and relationships you wouldn’t be able to see just by looking at the raw data. 🔍 Overall, segmentation is crucial for getting useful information from point…

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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 (💻 + 📦 + 📙 + ▶️)

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