Complete python tutorial to create supervised learning AI systems for semantic segmentation of unstructured 3D LiDAR point cloud data — Having the skills and the knowledge to attack every aspect of point cloud processing opens up many ideas and development doors. 🤖 It is like a toolbox for 3D research creativity and development agility. And at the core, there is this incredible Artificial Intelligence space that targets 3D scene understanding…

Tutorial for advanced visualization and interaction with big point cloud data in Python. (Bonus) Learn how to create an interactive segmentation “software”. — Data visualization is a big enchilada 🌶️: by making a graphical representation of information using visual elements, we can best present and understand trends, outliers, and patterns in data. And you guessed it: with 3D point cloud datasets representing real-world shapes, it is mandatory 🙂.

Hands-on tutorial to turn large point clouds into 3D voxels 🧊 with Python and open3d. Unlock an automation workflow for efficient 3D voxelization — What if we had a quick way to transform point clouds captured from reality into 3D meshes? And what if these 3D meshes were a voxel-based assembly? Is this something that makes sense? How can this help you for creative or professional purposes? 🤔 Let us dive in with a…

A Hands-on Beginner's Guide to Unlock the Power of Artificial Neural Networks in Python — In the realm of Artificial Intelligence, few technologies have garnered as much attention and achieved groundbreaking success as Artificial Neural Networks. Inspired by the human brain's complex interconnectedness (sorry for the swear word), these robust algorithmic structures have revolutionized, for real, the field of deep learning, propelling us into an…

A 10-step Python Guide to Automate 3D Shape Detection, Segmentation, Clustering, and Voxelization for Space Occupancy 3D Modeling of Indoor Point Cloud Datasets. — If you have experience with point clouds or data analysis, you know how crucial it is to spot patterns. Recognizing data points with similar patterns, or "objects," is important to gain more valuable insights. Our visual cognitive system accomplishes this task easily, but replicating this human ability through computational methods…

The Ultimate Python Guide to structure large LiDAR point cloud for training a 3D Deep Learning Semantic Segmentation Model with the PointNet Architecture. — The application field of 3D deep learning has snowballed in recent years. We have superb applications in various areas, including robotics, autonomous driving & mapping, medical imaging, and entertainment. When we look at the results, we are often awed (but not all the time 😁), and we may think: “I…

A full hands-on 3D Reconstruction tutorial using 3D Photogrammetry, Reality Capture, Meshroom, and Blender. — The times we live in are super exciting, even more so if you are interested in 3D stuff. We have the ability to use any camera, capture some image data from objects of interest, and turn them into 3D assets in the blink of an eye! This 3D Reconstruction Process…

A 5-Step Guide to create, detect, and fit linear models for unsupervised 3D Point Cloud binary segmentation: RANSAC implementation from scratch. — Have you ever wondered why we find so much geometry in the world surrounding us? No, you did not? Good news, it means you are sane. I tend to have weird interrogations about life and stuff 🙃. …

A complete hands-on python guide for creating 3D semantic segmentation datasets. Learn how to transform unlabelled point cloud data through unsupervised segmentation with K-Means clustering. — If you are on the quest for a (Supervised) Deep Learning algorithm for semantic segmentation — keywords alert 😁 — you certainly have found yourself searching for some high-quality labels + a high quantity of data points.