Point Cloud Processing Course 🦾
This course is a condensed hands-on lecture dedicated to providing you with focused content, immediately applied through an efficient fully-fledge open-source workflow. It treats point cloud data basics, engineering, semantization, structuration, analysis, visualisation and 3D modelling. The aim is to offer you the possibility to immediately integrate what you learnt in your personal or professional career.
Key concepts covered include:
- Point Cloud Basics: How to start processing point cloud datasets from different sensors (LiDAR, Laser Scanner, Photogrammetry, ...)
- Point Cloud Engineering: Create advanced feature extraction and registration routines
- Point Cloud Semantization: Develop a pure semi-automatic segmentation procedure followed by classification using Machine Learning
- Point Cloud Analysis and Visualisation: Create robust qualitative and quantitative quality reports supported by unique 2D/3D visualisations
- Point Cloud Data Structure and Modelling: Apprehend 3D data structures (Octree, kd-tree) to accelerate processing and for 3D Modelling
- 3D Python automation: Put all 5 concepts together to create endless automatic procedures for advanced point cloud processing
After completing this course, you will be able to produce advanced automatic point cloud processing workflows of your own by using free and open-source software and efficient python code blocks. The provided principles or workflow can also be extended to other paid software (Trimble, Leica Cyclone, Faro Scene, Flyvast, …). You will be able to create advanced 3D automatic modelling workflows, master the full point cloud processing pipeline and produce stunning rendering to showcase your new expertise. All resources, articles, point clouds and 3D models used on this course are shared via a folder.
Additionnal information
This course is designed for beginner to intermediate user, to show the process of creation of highly detailed 3D models with texture. No knowledge is required. You only need a computer, with some free HDD space (100+ Go), some RAM (8 Go+) and preferably a decent dedicated GPU.
