This tutorial targets 3D Point Cloud Feature Extraction for developing an interactive Python Segmentation App. The goal is to develop an end-to-end system that can abstract complex point clouds with pertinent features. These features are then used by a thresholding mechanism to extract parts of the 3D Point Cloud.
Point Cloud Feature Extraction: Tutorial Brief
In this tutorial on 3D Point Cloud feature extraction and interactive Python app development. We are going to do something very powerful, by establishing an end-to-end point cloud processing workflow.
We are going to consider a LiDAR Point Cloud and extract several features, both based on principle component analysis (PCA) and also Relative Featuring Techniques.
After that, we will leverage a Python library for 3D data to create create an interactive thresholding method that can extract part of the point Cloud based on the features extracted, directly in a python-based 3D application.
Objectives of Point Cloud Feature Extraction
We have two primary purposes.
- The first one, is to be able to have a workflow to label 3D data.
- The second one is to have the ability to leverage 3D machine learning, 3D deep learning and create AI models.
So whenever you’re ready, let’s get started on what I got cooking for you.
Feature Extraction Materials and Resources
🍇3D Dataset + Code: Google Drive Folder
📘Hands-on Guide: In the Editorial Phase
My 3D Recommendation 🍉
Extracting features from 3D point clouds is a crucial step toward developing deep workflows. This is the base of 3D Machine Learning, 3D Deep Learning and many Artificial Intelligence and 3D Data Science Systems. Resources on the topic are sparse, and you can find some in the literature or in some of the work I published (E.g. : Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods). If you want to use it in your solutions, I recommend to check out the 3D Point Cloud Course or the 3D Object Detection Course.
If you want to get a tad more toward application-first or depth-first approaches, I curated several learning tracks and courses on this website to help you along your journey. Feel free to follow the ones that best suit your needs, and do not hesitate to reach out directly to me if you have any questions or if I can give you some advice on your current challenges!
Open-Access Knowledge
- Medium Tutorials and Articles: Standalone Guides and Tutorials on 3D Data Processing (5′ to 45′)
- Research Papers and Articles: Research Papers published as part of my public R&D work.
- Email Course: Access a 7-day E-Mail Course to Start your 3D Journey
- Youtube Education: Not articles, not research papers, open videos to learn everything around 3D.
3D Online Courses
- 3D Python Crash Course (Complete Standalone). 3D Coding with Python.
- 3D Photogrammetry Course (Complete Standalone): Open-Source 3D Reconstruction.
- 3D Point Cloud Course (Complete Standalone): Pragmatic, Complete, and Commercial Point Cloud Workflows.
- 3D Object Detection Course (3D Python Recommended): Practical 3D Object Detection with 3D Python.
3D Learning Tracks
- 3D Segmentation Deck: From Classical 3D Segmentation to 3D Deep Learning and Unsupervised Applications
- 3D Collector’s Pack: Complete Course Track to address both 3D Application and Code Layers.
