3D Vision & Deep Learning
3D Segmentor OS
Build the deep learning stack that turns raw point clouds into classified, queryable 3D scenes. PointNet++, RandLA-Net, and a shipped app. 5 modules.
Note from Florent
Segmentation is where raw scans become actionable. I’ve packed my complete workflow into this OS: from RANSAC to deep learning, you’ll ship a segmentation app that actually runs in production.
What you will build
Module 01
Foundations and object detection
Set up your deep learning environment. Master the 3D Python stack. Build your first model.
Module 02
Semantic segmentation
Train and deploy deep networks for 3D semantic segmentation. PointNet++, RandLA-Net.
Module 03
Algorithm forge
Nine algorithmic segmentation methods. Region growing, DBSCAN, Euclidean clustering.
Module 04
Unsupervised and labelling
Segment without labels. SegmentAnything 3D, foundation model integration, and automation.
Module 05
Deployed segmentation app
Package your segmentation engine into a working app. Python backend, WebGL viewer.
Your starting resources
Additional guides and code packs unlock as you progress through each module.
