Our Scientific articles
Discover our scientific articles by the two doctors of the Flyvast team. Feel free to visit their articles in ResearhGate!
Many of the articles talk about features used by Flyvast. We invite you to have a look at the solutions we propose.
3D Machine Learning 201 Guide: Point Cloud Semantic Segmentation
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. 🏡 It is particularly relevant due to its importance for many applications, such as self-driving cars, autonomous robots, 3D mapping, virtual reality, and the Metaverse. And if you are an automation geek like me, it is hard to resist the temptation to have new paths to answer these challenges! This tutorial aims to give you what I consider the essential footing to do just that: the knowledge and code skills for developing 3D Point Cloud Semantic Segmentation systems. But actually, how can we apply semantic segmentation? And how challenging is 3D Machine Learning?
3D Point Cloud Clustering Tutorial with K-means and Python
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.
In our 3D data world, the unlabelled nature of the 3D point clouds makes it particularly challenging to answer both criteria: without any good training set, it is hard to “train” any predictive model.
Should we explore python tricks and add them to our quiver to quickly produce awesome 3D labeled point cloud datasets?