3D Tutorials to Learn 3D Data Processing.

3D Tutorials to learn 3D point clouds and 3D Data Science

I create 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. 🫶

3D Learning Resources: Top 5 Picks 🍒

✅3D Geospatial Data Integration with Python: The Ultimate Guide (incl. 💻 code, 📂 data and 🖼️ visuals)
✅The Blender Handbook for 3D Point Cloud Visualization and Rendering (incl. 💻 code, 📂 data and 🖼️ visuals)
✅3D Deep Learning Essentials: Ressources, Roadmaps and Systems (incl. 💻 code, 📂 data and 🖼️ visuals)
✅Segment Anything 3D for Point Clouds: Complete Guide (SAM 3D) (incl. 💻 code, 📂 data and 🖼️ visuals)
✅3D Point Cloud Shape Detection for Indoor Modelling (incl. 💻 code, 📂 data and 🖼️ visuals)

3D Tutorials: Latest Releases (Weekly) 🐠

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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 as well!

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. 🐉

3D Tutorial 004. 3D Point Cloud Unsupervised Clustering with 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. ☣️

3D Tutorial 002. 3D Point Clouds in Blender: Starter Guide.

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.

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