Real-Time 3D Point Cloud Classification for 3D Shapes (PCA + Random Forests): Micro Course

Описание: 1. 📕 Early-release of my new book with O'Reilly: https://www.oreilly.com/library/view/...

2. 🎓 Learn 3D Data and GeoAI: https://learngeodata.eu

Learn how to build a lightning-fast 3D point cloud classifier using Principal Component Analysis (PCA) and Random Forest that achieves 92% accuracy without deep learning. This tutorial shows you how to extract geometric features from point clouds and classify buildings, ground, and vegetation in real-time using Python. Based on techniques from the "3D Data Science with Python" book and implemented in the open-source 3D Segmentor OS project. Perfect for LiDAR processing, autonomous vehicles, and 3D mapping with limited computational resources.

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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: 3D Point Cloud Classification using PCA with Random Forest
[00:50] Learning Outcomes: What you'll be able to achieve after this tutorial.
[02:05] Setup: Explanation of the required environment, Anaconda virtual environment, and needed libraries (NumPy, scikit-learn, Open3D, readPLY).
[03:45] Creating a 3D Visualizer: Introduction to a helper function for visualizing point clouds and testing it with random data.
[05:00] Outlier Removal: Explanation of the Outlier Removal function using K-Nearest Neighbors.
[07:54] Normalization: Point Cloud Normalization.
[10:10] PCA Feature Extraction: In-depth overview of Principal Component Analysis (PCA), its relevance, mathematical background, and implementation for feature extraction from point clouds.
[16:30] Testing shapes: Executing the PCA feature computation across multiple shapes, with details in the console for each element
[18:50] Model definition: Random forest model definition, describing important parameters
[22:26] Dataset Creation: Demonstrating simulation of training data (features and labels) by creating synthetic spheres, cylinders, and planes.
[23:40] Training: Training the classifier, printing out the relevant statistics about the trained model.
[25:18] Inference Function Pipeline: Discussion and explanation of creating an inference function to apply the trained model to new, unseen data.
[27:20] Testing Inference on Dummy Data: Testing the inference on simulated data, showing the process of classifying a generated plane and its classification time.
[30:05] Running the Inference on Actual Generated Shapes: Loading 3D shapes (cube, cylinder, plane, sphere) from files and running them through the inference pipeline to classify them.
[32:25] Extending to Super Nice Ideas: Discussion on ways to extend and improve the current system, focusing on model creation