3D Clustering Mastery: How to Segment Point Clouds with Graph Theory

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As you know, manually segmenting point clouds to identify objects is a tedious process, especially when dealing with massive datasets and memory limitations. It becomes even more challenging when trying to isolate individual objects that might be overlapping or in close proximity to one another. In this video, I present a solution using graph theory to automate the segmentation process and efficiently cluster objects within a point cloud. You will learn how to construct a graph from your point cloud data, analyze its connected components, and visualize the results to achieve automatic segmentation using Python libraries

This video is an excellent resource for anyone who wants to learn how to build 3D data tools.

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If we haven’t yet before - Hey 👋 I’m Florent, a professor-turned-entrepreneur, and I’ve somehow become one of the most-followed 3D experts. Through my videos here on this channel and my writing, I share evidence-based strategies and tools to help you be better coders and 3D innovators.

📗 CHAPTERS
[00:00] Introduction to Graphs for 3D
[01:52] Required Python Libraries
[03:15] Problem Statement and Approach
[04:38] Data Pre-processing and Illustration
[06:30] Setting Up the Python Environment
[07:46] Loading and Preparing Point Cloud Data
[08:47] Simulating a 2D Point Cloud Dataset
[09:47] Introduction to Graph Theory and Concepts
[13:39] Constructing a Graph from Point Cloud Data
[17:13] Analyzing Connected Components
[18:31] Plotting and Visualizing the 2D Graph
[21:00] Applying Graph Analysis to the Real-World Dataset
[22:28] Visualizing and Clustering Results
[24:05] Conclusion