/3D-DATA-COURSE

3D Data Processing Courses for Ditigal Twins. ITC UTWENTE

MIT LicenseMIT

3D-DATA-COURSE

Geospatial 3D Data Processing Courses for Digital Twins. ITC, University of Twente.

3D Data Processing Open Course

3D Data Processing Logo

Welcome to the 3D Data Course repository! This repository aims to provide a comprehensive set of tutorials on 3D Data Processing using Python. Whether you're a beginner or an experienced practitioner, this resource will guide you through the fundamentals and advanced concepts of 3D for Digital Twins. This Open Course initiative is made possible by the financial contribution from the digital twins @ITC -project granted by the ITC faculty of the University of Twente. We are thankful for the support, without which this could not have been possible.

Table of Contents

  1. Introduction to 3D Data Processing for Digital Twins
  2. Getting Started
  3. Installation
  4. Course Tutorials
  5. Examples
  6. Contributing
  7. License

Introduction

3D Data Processing has gained significant traction in various fields, notably geospatial mapping and digital twins. This repository serves as a course and code learning hub for understanding and implementing 3D data processing techniques using Python and Open-Source Tools.

Getting Started

Before diving into the tutorials, make sure you have the necessary tools and libraries installed. Please refer to the Installation section for detailed instructions.

Installation

To get started with 3D Data Processing, you'll need to set up your environment. Each code package is grounded with a how-to guide accessible on this Medium Page. You then have a section dedicated to the local setup. It usually involves this:

# Clone the repository
git clone https://github.com/username/3d-deep-learning.git

# Navigate to the project directory
cd 3d-data-processing

# Install miniconda with Python version 3.10

# Create a virtual environment (optional but recommended)
conda create -n DEEPTUTO python=3.10

# Activate the virtual environment
conda activate

# Install dependencies using requirements (if set-up)
pip install -r requirements.txt

#Install dependencies using the given libraries in the Medium Article
pip install numpy matplotlib rasterio laspy open3d

Tutorials

Tutorial 1: 3D LiDAR Workflows

The tutorial covers Python Automation combining 3D Point Clouds, Meshes, and Voxels for advanced analysis.

For starting the tutorial, please refer to the tutorials directory, and chose the relevant one

Tutorial 1: Understanding 3D Data

In this tutorial, we'll cover the basics of working with 3D data, including formats, visualization, and common preprocessing techniques.

📖 3D Python Workflows for LiDAR City Models: A Step-by-Step Guide

Tutorial 2: ALS LiDAR Classification with Deep Learning

The Ultimate Python Guide to structure large LiDAR point cloud for training a 3D Deep Learning Semantic Segmentation Model with the PointNet Architecture.

📖 3D Deep Learning Python Tutorial: PointNet Data Preparation

Tutorial 3: 3D Indoor Unsupervised Segmentation (SAM)

How to build a Semantic Segmentation Application for 3D Point Clouds leveraging SAM and Python. Bonus: Code for Projections and…

Coming soon.

Tutorial 4: 3D Point Cloud Detection for Indoor Mapping

A 10-step Python Guide to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Space Occupancy 3D Modeling of Indoor Point Cloud Datasets.

📖 3D Point Cloud Shape Detection for Indoor Modelling

Tutorial 5: 3D Data Integration

Integrate 3D spatial data with Python and explore essential processing steps for reading, loading, transforming, and visualizing 3D point clouds, meshes, cityGML models, voxels, vector data, satellite raster, and 360 images.

📖 3D Geospatial Data Integration with Python: The Ultimate Guide

Tutorial 6: Deploying 3D Models in Applications

Coming soon.

Tutorial 7: Deploying 3D Models in Applications

Coming soon.

Tutorial 8: Deploying 3D Models in Applications

Coming soon.

Tutorial 9: Deploying 3D Models in Applications

Coming soon.

Contributing

We welcome contributions! If you have an idea for a new tutorial or want to improve existing content, please refer to the contributing guidelines.

Main author:

  • Dr. Florent Poux

Course Tutorial Co-Authors:

  • Dr. Sander Oude Elberink
  • Dr. Mila Koeva
  • Dr. Ville Lehtola
  • Dr. Pirouz Nourian
  • Dr. Paulo Raposo
  • Prof G. Vosselman

License

This open course initiative is made possible by the financial contribution from the digital twins @ITC -project granted by the ITC faculty of the University of Twente. We thank the initiative very much. This repository is licensed under the MIT License.

Feel free to reach out with any questions, feedback, or suggestions. Happy learning! 🚀