3D Tutorials: Videos, Articles, Code, Data.
I curated on this page 3D tutorials that I created that address a single standalone skill that I believe is very important to wield properly. These are all open-access, and I curate the most relevant ones with current needs in the market. If you have any recommendations, feel free to email me directly through my 3D newsletter.
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.
3D Point Clouds to 3D Mesh
In this 3D Tutorial, we learn how to create stunning 3D meshes from point clouds using Python. We focus on how to use the popular Python library Open3D best to create a 3D mesh from a point cloud. We also cover how to visualize the mesh using CloudCompare or MeshLab.
Materials and 3D Tutorial Resources
🍇3D Dataset: Google Drive Folder
📘Hands-on Guide: Medium Article
My 3D Recommendation 🍉
Turning 3D Point Clouds into 3D Meshes is an integral cog of a lot of different workflows. While some software offers you the possibility to do just that very simply without any coding knowledge when you need more flexibility, there is no alternative: we need to leverage programming. But the good news is that 3D Python is a great way to enter the realm of programming while quickly getting amazing results as we get here. If you want to extend this branch, I warmly recommend following up the 3D crash course: 3D Python Crash Course.
