Teach machines to see the world in three dimensions. These guides cover the algorithms that turn raw pixels and points into geometric understanding, from feature matching to pose estimation.
Label a 3D point cloud with any word you type. This complete guide walks the open-vocabulary 3D segmentation stack in Python: pretrained SAM cuts the scene, CLIP scores each region, and a scene graph lets an LLM answer questions about the space.
Open-Vocabulary 3D Segmentation 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 »
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 »
Learn to build intelligent 3D scene graphs from point clouds using Python, NetworkX, and OpenUSD. Complete tutorial access with LLM integration for spatial AI. The hardest part of spatial AI isn’t capturing the data. It’s making that data actually intelligent. You’ve got millions of perfectly labeled 3D points, but when you try to build something
3D Scene Graphs for Spatial AI with NetworkX and OpenUSD Read More »
