Make your 3D data visible and shareable. Rendering techniques, web viewers, and interactive experiences that bring point clouds and meshes to any browser.
The complete guide to going from a raw point cloud to a clean, watertight 3D mesh in Python. Estimate normals, run Poisson in Open3D, trim the blobs, compare scans, and render millions of points.
Point Cloud to Mesh Complete Guide Read More »
A cloud bigger than your memory still spins at sixty frames a second. The trick is the one your maps app already plays on you: a pyramid of pre-cut tiles, called an octree, that loads detail only where you are looking. Here is the full machinery, with a runnable Python demo.
By Dr. Florent Poux, PhD in 3D geospatial sciences | Author, 3D Data Science with Python (O’Reilly) | Published 11 February 2025 Complete Tutorial to Automate 3D Data Visualization. Use Python to convert point clouds and 3D models into GIFs & MP4s for easy sharing and collaboration. As a data scientist, you know that effectively […]
How To Generate GIFs from 3D Data with Python 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 16 February 2021 Visualise Massive point cloud in Python. Point clouds past the 10 million point mark are too dense for classical visualisation libraries such as Matplotlib, so this tutorial uses PPTK, which relies on an […]
Visualise Massive point cloud in Python Read More »
