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 »
Your 3D bookmarks are a pile of tutorials that never reference each other. Here is the whole field of 3D spatial AI with Python, six skills wired into one pipeline you can build.
3D Spatial AI The 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 »
From the NXT BLD 2026 stage and the reality capture panel: the local-first cognition stack for 3D spatial AI for AEC. Smart point cloud in, IFC and decisions out, in three days, not six weeks.
3D Spatial AI for AEC: The Offline Stack That Replaces 6 Weeks of BIM Work Read More »
A complete Python pipeline for 3D point cloud labeling from phone photos using interactive painting, pinhole projection, and KD-tree fusion — no GPU required.
3D Point Cloud Labeling from Photos with Python (and why AI can’t design it for you) Read More »
It was a Tuesday when I got the call. A €40,000 project was completely stalled, burning cash every hour. The reason? The one person who knew the specific sequence of clicks to get data from their laser scanner software into AutoCAD was on vacation. The entire, expensive, high-tech 3D data pipeline was held hostage by
3D Pipeline Architecture: A Founder’s Blueprint 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 article compares the top 11 cloud tools that leverage 3D Generative AI. These 3D solutions simplify workflows and open new avenues for creative expression. These tools include Meshy, 3D AI Studio, Masterpiece X, Alpha 3D, Sloid, 3DFY AI, Spline, Luma AI, Rococo Vision, Ponzu, and Deep Motion. Each tool offers unique features like text-to-3D,
3D Generative AI: 11 Tools (Cloud) for 3D Model Generation Read More »
This learning piece provides a step-by-step guide for developing your 3D Object Recognition application, from data collection to deployment. Learn about the different approaches to data collection and preparation, the importance of feature engineering, and the process of selecting and training a machine learning model. Discover best practices for evaluating and deploying your model’s performance
Building a 3D Object Recognition Algorithm: A Step-by-Step Guide Read More »
