By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 27 December 2024 Dive into the fascinating world of 3D with deep learning, where objects come alive and possibilities are beyond 2DÂ pixels. Few technologies can change the landscape of Computer Science at the pace and impact […]
3D Deep Learning Essentials: Ressources, Roadmaps and Systems Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 8 December 2024 We dive into SuperPoint Transformer, a novel approach for 3D semantic segmentation presented in the research paper “Efficient 3D Semantic Segmentation with Superpoint Transformer“. We also explore the core concepts, examine the research […]
Tutorial for 3D Semantic Segmentation with Superpoint Transformer Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 29 November 2024 Python Tutorial for Euclidean Clustering of 3D Point Clouds with Graph Theory. Fundamental concepts and sequential workflow for unsupervised segmentation. As you are developing the next generation of AI systems, you face a […]
Python Guide for Euclidean Clustering of 3D Point Clouds Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 4 November 2024 Designing a point cloud workflow is a powerful first-hand approach in 3D data projects. This article explores how processing massive point clouds efficiently isn’t about having more computing power. It’s about being more […]
A Quick Dive into Modern Point Cloud Workflow Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 21 October 2024 The average LiDAR scan contains 250+ million points. Visualizing and sharing this data efficiently is a significant challenge for many professionals. This tutorial provides a no-code solution to visualize and manage massive point […]
How to Quickly Visualize Massive Point Clouds with a No-Code Framework Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 19 July 2024 RANSAC detects spheres and planes in noisy 3D point clouds by repeatedly fitting a candidate model to a random sample of points and keeping the fit that explains the largest number of inliers. […]
3D Shape Detection with RANSAC and Python (Sphere and Plane) Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 10 June 2024 In this tutorial, I provide a detailed walkthrough on how to generate 3D Voxels from Point Clouds using Python. At the end, you will be able to automate the creation of 3D Voxel […]
3D Point Cloud to Voxels: Python Voxelization Tutorial Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 24 January 2024 This tutorial targets 3D Point Cloud Feature Extraction for developing an interactive Python Segmentation App. The goal is to develop an end-to-end system that can abstract complex point clouds with pertinent features. These […]
Point Cloud Feature Extraction: Complete Guide Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 19 January 2024 3D Modelling: Point Cloud to 3D Mesh In this 3D Tutorial, we learn how to create stunning 3D meshes from point clouds using Python and 3D Modelling techniques. We focus on how to […]
3D Modelling: Point Cloud to 3D Mesh Read More »
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 18 January 2024 3D Point Cloud Vectorization for LiDAR City Models This hands-on approach is standalone and covers the process of LiDAR Vectorization. We then focus on City Model Automatic Generation (LoD 0) in 5 main […]
Vectorization of 3D Point Cloud for LiDAR City Models Read More »
