How to Compute PCA and Visualize 3D Point Cloud with Python (Principal Component Analysis 3D Course)

This tutorial highlights how we can leverage Principal Component Analysis (PCA) for 3D Point Cloud Scene Understanding and Segmentation. It is an extract of the course 3D Detector, a 3D Object Detection Course. Have fun coding this project! 🍿 NEXT STEPS: Code a 3D Point Cloud Segmentation Solution with Python: https://youtu.be/-OSVKbSsqT0?si=XxM7yXBMcBYRYPf5 Finish the 3D Tutorial Series: https://learngeodata.eu/3d-tutorials/ Dive in Expert articles: https://medium.com/@florentpoux Become a 3D Data Science Expert: https://learngeodata.eu 🙋 FOLLOW ME Linkedin: https://www.linkedin.com/in/florent-poux-point-cloud/ Github: https://github.com/florentPoux Research: https://scholar.google.com/citations?user=eoyJ6eYAAAAJ&hl=en WHO AM I? If we haven’t yet before - Hey 👋 I’m Florent, a professor-turned-entrepreneur, and I’ve somehow become the world’s most-followed 3D Python Expert. 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 📘 [00:00:00]: Introduction to Principal Component Analysis (3D) [00:01:25]: Overview of the Workflow for 3D Data Processing [00:04:31]: Importing 3D Python Libraries [00:05:12]: Loading the Point Cloud Dataset [00:07:54]: DBSCAN and K-NN Segment Data Preparation [00:09:52]: Cluster-based PCA for Point Cloud [00:16:39]: Combine Vectors and Point Clouds [00:18:45]: Creating the DrawPCA Function [00:20:35]: Automation through 3D PCA Loop [00:22:29]: 3D Feature Extraction Loop [00:27:40]: Point Cloud with Eigen Features Export [00:28:10]: Feature-based 3D Point Cloud Visualization [00:29:09]: PCA for 3D Point Clouds Conclusion