3D Data Processing Tutorials
I write 3D tutorials to help you develop 3D Data Processing Skills. These guides are all open-access and take on various shapes (articles, code, notebooks, videos, datasets). Feel free to email me directly through my 3D newsletter if you have any recommendations, questions, or want to join the sharing force. 🫶
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Latest Releases
Editor’s Pick
Videos
3D Tutorials: Latest Releases
I write tutorials and guides every month to help you develop new solutions. I target topics such as 3D reconstruction, 3D Processing, Spatial AI, Point Clouds, 3D Object Recognition, and more.
- Python Guide for Euclidean Clustering of 3D Point Clouds
Python Tutorial for Euclidean Clustering of 3D Point Clouds with Graph Theory. Fundamental concepts and sequential workflow for unsupervised segmentation.… Read more: Python Guide for Euclidean Clustering of 3D Point Clouds - A Quick Dive into Modern Point Cloud Workflow
Designing a point cloud workflow is a powerful first-hand approach in 3D data projects. This article explores how processing massive… Read more: A Quick Dive into Modern Point Cloud Workflow - 3D Mesh from Point Cloud: Python with Marching Cubes Tutorial
This tutorial dives deep into the Marching Cubes algorithm, a powerful technique for meshing 3D point clouds using Python. We… Read more: 3D Mesh from Point Cloud: Python with Marching Cubes Tutorial - How to Quickly Visualize Massive Point Clouds with a No-Code Framework
The average LiDAR scan contains 250+ million points. Visualizing and sharing this data efficiently is a significant challenge for many… Read more: How to Quickly Visualize Massive Point Clouds with a No-Code Framework - Building a 3D Object Recognition Algorithm: A Step-by-Step GuideThis learning piece provides a step-by-step guide for developing your 3D Object Recognition application, from data collection to deployment. Learn… Read more: Building a 3D Object Recognition Algorithm: A Step-by-Step Guide
- 3D Generative AI: 11 Tools (Cloud) for 3D Model Generation
This article compares the top 11 cloud tools that leverage 3D Generative AI. These 3D solutions simplify workflows and open… Read more: 3D Generative AI: 11 Tools (Cloud) for 3D Model Generation - 3D Shape Detection with RANSAC and Python (Sphere and Plane)
This tutorial will walk you through the process of detecting spheres and planes in 3D point clouds using RANSAC and… Read more: 3D Shape Detection with RANSAC and Python (Sphere and Plane) - Tutorial for 3D Semantic Segmentation with Superpoint Transformer
We dive into SuperPoint Transformer, a novel approach for 3D semantic segmentation presented in the research paper “Efficient 3D Semantic… Read more: Tutorial for 3D Semantic Segmentation with Superpoint Transformer - 3D Python Environment Setup: 7-Steps Guide for Beginners
This guide helps you get started with Python for 3D Data Science. It covers the essential steps in setting up… Read more: 3D Python Environment Setup: 7-Steps Guide for Beginners - 2D Image to 3D ModelThis Live Course / Workshop series will give you a linear, hands-on solution for turning a monocular 2D Image to… Read more: 2D Image to 3D Model
- 3D Point Cloud to Voxels: Python Voxelization TutorialIn this tutorial, I provide a detailed walkthrough on how to generate 3D Voxels from Point Clouds using Python. At… Read more: 3D Point Cloud to Voxels: Python Voxelization Tutorial
- 3D Gaussian Splatting: Hands-on Course for BeginnersThis Tutorial targets 3D Reconstruction and 3D Real-Time Visualization leveraging 3D Gaussian Splatting. In the end, you will be able… Read more: 3D Gaussian Splatting: Hands-on Course for Beginners
- 3D Reconstruction from a Single Image: Tutorial
This tutorial targets Monocular Depth Estimation for 3D Reconstruction (Point Cloud, 3D Mesh). In the end, you will be able… Read more: 3D Reconstruction from a Single Image: Tutorial - 3D Deep Learning Essentials: Ressources, Roadmaps and Systems
Dive into the fascinating world of 3D with deep learning, where objects come alive and possibilities are beyond 2D pixels. Few… Read more: 3D Deep Learning Essentials: Ressources, Roadmaps and Systems - Point Cloud Feature Extraction: Complete GuideThis tutorial targets 3D Point Cloud Feature Extraction for developing an interactive Python Segmentation App. The goal is to develop… Read more: Point Cloud Feature Extraction: Complete Guide
3D Learning Resources: Editor’s Pick
The following articles are deep-dive guides curated and published in Towards Data Science (Medium). They contain visuals, code, and a step-by-step workflow.
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3D Data Processing: Video Tutorials Serie
Below, you will find three video tutorials that are shared openly. Feel free to explore the YouTube channel for new releases every month!
3D Point Cloud Unsupervised Clustering with Python
I share a hands-on Python approach to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Point Cloud Datasets. In this case, we study an example of an indoor dataset. By the end, you’ll have a solid understanding of how to work with 3D point cloud datasets and perform advanced 3D shape recognition tasks using Python. 🐉
Materials and 3D Tutorial Resources
🍇3D Dataset: Google Drive Folder
📘Hands-on Guide: Medium Article
My 3D Recommendation 🍉
Having the ability to detect shapes and segment 3D point clouds is adds a lot of value to any workflow. Specifically, this permits to label 3D datasets with more efficiency, have unsupervised segmentation approaches, and extract information in an autonomous fashion. If you want to push this concept to its limit, I recommend the 3D Segmentation Deck that dives deep in 3D Object Recognition and Segmentation: 3D Segmentation Deck.
3D Point Cloud Processing Starter Pack
I share a hands-on Python approach to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Point Cloud Datasets. In this case, we study an example of an indoor dataset. By the end, you’ll have a solid understanding of how to work with 3D point cloud datasets and perform advanced 3D shape recognition tasks using Python. 🐉
Materials and 3D Tutorial Resources
🍇3D Dataset: Access Open Data Portals (E.g. OpenTopography)
My 3D Recommendation 🍉
Whenever you are starting a 3D Data Project that involves Point Clouds, it is nice to know you have an optimized setup. Once this is the case, the next logical step is to develop end-to-end point cloud workflows, as shown in the course: 3D Point Cloud Processor.
3D Point Clouds to Blender
In this 3D tutorial, you’ll learn how to integrate and process 3D Point Clouds in Blender. We address the complete workflow from point data I/O to scene setup and rendering in Blender. I illustrate the case of an Indoor Extraction Scenario where you would need to create a stunning rendering to show off the best route to take to gather a dangerous artifact. ☣️
Materials and 3D Tutorial Resources
🍇3D Dataset: Google Drive Folder
📘Hands-on Guide: Medium Article (Coming soon)
My 3D Recommendation 🍉
The ability to adapt visuals and renderings of 3D Point Clouds is really empowering. Indeed, it means that you could cut down on heavy 3D Modelling workflow or quickly go from on-site data acquisition to visual presentation to stakeholders. This perfectly fits within the boundaries of 3D Point Cloud Workflows. They are covered in depth in the course: 3D Point Cloud Processor. I encourage you to unlock that if you want to extend your reach and value to your team/vision.