Classify LiDAR point clouds into meaningful categories using Random Forests, K-Means clustering, and scikit-learn pipelines. 15 lessons that take you from raw scans to fully labeled 3D scenes. You get:

15 Progressive Lessons: Start with raw LiDAR data, extract geometric features, train classifiers, and produce publication-ready segmented point clouds.

3 ML Approaches Compared: Implement unsupervised clustering, supervised Random Forests, and hybrid pipelines so you know which method fits your data.

Full Python Codebase: Every script runs end-to-end on the included LiDAR datasets. No setup headaches, no missing dependencies.

24/7 Direct Support: Questions about feature engineering or model tuning? Get direct email help from the instructor.

Lifetime Access + Commercial License: Apply the code and trained models in your professional projects. All future course updates included.

14-day Money Back Guarantee

Build PointNet from the ground up: architecture, data preparation, training, and inference on real 3D point cloud data. 8 focused lessons that give you a working deep learning pipeline for 3D. You get:

8 Hands-On Lessons: Implement PointNet step by step in PyTorch, from the T-Net alignment module to the full classification and segmentation heads.

Real 3D Datasets Included: Train on actual point cloud data, not toy examples. Includes preprocessed datasets ready for immediate use.

Classification + Segmentation: Two complete pipelines covering 3D object classification and per-point semantic segmentation with evaluation metrics.

24/7 Direct Support: Get unstuck fast with direct email access to the instructor for debugging, architecture questions, or custom use cases.

Lifetime Access + Commercial License: Keep all code, models, and data forever. Use them in client projects or internal tools. All updates included.

14-day Money Back Guarantee

Harness the power of Meta’s billion-parameter Vision Foundation Model to automatically label unstructured 3D point clouds without a single training epoch.

⏱️ Duration: 4 Hours | 🎚️ Level: Advanced | 🛠️ Core Stack: Python, Meta SAM, Open3D

We engineer a mathematical pipeline to project 3D point clouds into 2D planes, run the SAM inference, and re-project the semantic masks back into 3D space.

What you get :

⚙️ Built Systems: Deploy a complete 3D-to-2D-to-3D SAM projection script.

📈 Scaled Value: Achieve state-of-the-art segmentation on custom point clouds instantly.

Build a Lean 3D Object Detection System with Python. This Online Blueprint delivers a complete 3D Recognition System for your 3D Point Cloud Data. It includes:

1x 3D Detector Course: Self-paced, regular updates following technology evolutions.

40+ Resources and Tools: 3D Datasets, Scripts, Software, Cheat Sheets, Articles, Tutorials.

24 / 7 Premium Support: My direct mail support.

1x Commercial License: Use materials (code, models, data) commercially.

14-day Money Back Guarantee