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Notes & Deep Dives

Longer-form notes from my desk. Point clouds, neural rendering, spatial AI. The things I'd rather write down than explain twice.

Point Cloud Level of Detail in Python: Octrees, Frustum Culling, and Out-of-Core Rendering

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.

01 Jul 2026 Prof. Florent Poux
Read more about Point Cloud Level of Detail in Python: Octrees, Frustum Culling, and Out-of-Core Rendering
3D Point Cloud Segmentation Complete Guide

3D Point Cloud Segmentation Complete Guide

A complete, practical 101 on 3D point cloud segmentation in Python: what it is, the method families, real Open3D code…

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3D Reconstruction Complete Guide

3D Reconstruction Complete Guide

A folder of phone photos already holds a 3D model. This complete guide walks 3D reconstruction in Python end to…

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LiDAR and Point Cloud Processing Complete Guide

LiDAR and Point Cloud Processing Complete Guide

The complete 101 guide to LiDAR point cloud processing in Python. Read LAS files with laspy, index with an Open3D…

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3D Spatial AI The Complete Guide

3D Spatial AI The Complete Guide

Your 3D bookmarks are a pile of tutorials that never reference each other. Here is the whole field of 3D…

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Point Cloud Level of Detail in Python: Octrees, Frustum Culling, and Out-of-Core Rendering

Point Cloud Level of Detail in Python: Octrees, Frustum Culling, and Out-of-Core Rendering

A cloud bigger than your memory still spins at sixty frames a second. The trick is the one your maps…

Read More →
Open-Vocabulary 3D Semantics in Python: SAM, CLIP, and DINOv2 (No Training)

Open-Vocabulary 3D Semantics in Python: SAM, CLIP, and DINOv2 (No Training)

Turn a careless phone video into a labeled, scaled, measurable 3D room with pretrained SAM, CLIP, and DINOv2, and not…

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