The craft of turning billions of points into structured knowledge. Filtering, segmentation, clustering, and feature extraction workflows, all with hands-on Python code.
The complete 101 guide to LiDAR point cloud processing in Python. Read LAS files with laspy, index with an Open3D KDTree, filter ground with CSF, build a DTM.
LiDAR and Point Cloud Processing 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.
Turn a careless phone video into a labeled, scaled, measurable 3D room with pretrained SAM, CLIP, and DINOv2, and not one training run. A nine-step open-vocabulary 3D semantics pipeline in Python.
Open-Vocabulary 3D Semantics in Python: SAM, CLIP, and DINOv2 (No Training) Read More »
This tutorial teaches you how to develop a Synthetic point cloud generation engine in Python using NumPy and Open3D, enabling the creation of unlimited labeled 3D data for machine learning. By automating the process, you can save time, reduce costs, and generate realistic training datasets without expensive equipment or manual labeling.
Synthetic Point Cloud Generation of Rooms: Complete 3D Python Tutorial Read More »
This tutorial dives deep into the Marching Cubes algorithm, a powerful technique for meshing 3D point clouds using Python. We transform a point cloud into a 3D mesh, experiment with various parameters, and build a simple web app with a graphical user interface (GUI). This method bypasses the limitations of other reconstruction techniques like Poisson
3D Mesh from Point Cloud: Python with Marching Cubes Tutorial Read More »
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 critical bottleneck: efficiently segmenting 3D point clouds to extract meaningful objects. This process is often manual, time-consuming, and prone to errors, especially
Python Guide for Euclidean Clustering of 3D Point Clouds Read More »
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 innovative with the resources you have. After analyzing hundreds of real-world projects, let me share identified patterns that consistently deliver better results
A Quick Dive into Modern Point Cloud Workflow Read More »
This tutorial will walk you through the process of detecting spheres and planes in 3D point clouds using RANSAC and Python. This is because 3D shape detection is a crucial task in computer vision and robotics, enabling machines to understand and interact with their environment. Specifically, RANSAC is widely used for robust model fitting in
3D Shape Detection with RANSAC and Python (Sphere and Plane) Read More »
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 Datasets from Large Point Clouds automatically. How to Generate 3D Voxels from Point Clouds with Python Welcome to this tutorial on 3D
3D Point Cloud to Voxels: Python Voxelization Tutorial Read More »
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 features are then used by a thresholding mechanism to extract parts of the 3D Point Cloud. Point Cloud Feature Extraction: Tutorial Brief
Point Cloud Feature Extraction: Complete Guide Read More »
