The craft of turning billions of points into structured knowledge. Filtering, segmentation, clustering, and feature extraction workflows, all with hands-on Python code.
How to get an as-built floor plan from a 3D scan you can trust: four methods compared, the floor area standard to name, what automated extraction misses, and the spec to send your surveyor.
How to Make an As-Built Floor Plan From a 3D Scan Read More »
The complete 101 on agentic spatial workflows: what the model is allowed to decide, how to type your tools so it cannot misread them, the validator that runs before any data is touched, and the two verification layers that decide what ships.
Agentic Spatial Workflow Orchestration Complete Guide Read More »
The complete 101 on multimodal foundation models for 3D assets: why 3D and text pairs are scarce, the four bridges from points into a pretrained backbone, what to freeze, and how to evaluate honestly.
Multimodal Foundation Models for 3D Assets Complete Guide Read More »
The complete 101 guide to point cloud ingestion in Python. Probe LAS headers with laspy, resolve CRS and datums with pyproj, measure real spacing, and emit one normalized record.
3D Sensor Abstraction and Point Cloud Ingestion Guide Read More »
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 complete, practical 101 on 3D point cloud segmentation in Python: what it is, the method families, real Open3D code for RANSAC and DBSCAN, and the parameters that decide your results.
3D Point Cloud Segmentation Complete Guide Read More »
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 »
