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Wilson Mar

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Identify what has changed in your 3D spatial world.

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Overview

Use Case

Imagine sending a tiny drone into a hotel room. A minute later, you have a list of what was left behind and what needs to be replenished, with an estimate of how long it would take, with and without robots doing the work.

https://www.linkedin.com/learning/from-drone-to-autocad-the-workflow/working-with-point-clouds-in-autocad

Welcome to the world of 3D data processing: spatial algorithms on digital twin semantic representation, statistical analysis, and computational geometry that transforms raw point cloud data into actionable intelligence.

3D change detection finds meaningful differences while filtering out everything else.

https://medium.com/data-science-collective/smart-3d-change-detection-python-tutorial-for-point-clouds-0dfd9945eb6a

“traditional methods of tracking these changes remain painfully manual, error-prone, and time-consuming”

a Python-powered system that:

Automatically aligns multi-temporal 3D scans with millimeter precision Identifies missing or damaged building components in seconds Generates reports that stakeholders understand Scales from single buildings to entire infrastructure networks

Algorithms and techniques enable spatial reasoning and decision making.

References

https://learning.oreilly.com/library/view/3d-data-science/9781098161323/ BOOK: “3D Data Science with Python” April 2025 by Florent Poux, Ph.D. (Toulouse, France)

  1. Introduction to 3D Data Science
  2. Resources and Software Essentials
  3. 3D Python and 3D Data Setup
  4. 3D Data Representation and Structuration
  5. Developing a Multimodal 3D Viewer with Python
  6. Point Cloud Data Engineering
  7. Building 3D Analytical Apps
  8. 3D Data Analysis
  9. 3D Shape Recognition
  10. 3D Modeling: Advanced Techniques
  11. 3D Building Reconstruction from LiDAR Data
  12. 3D Machine Learning: Clustering
  13. Graphs and Foundation Models for Unsupervised Segmentation
  14. Supervised 3D Machine Learning Fundamentals
  15. 3D Deep Learning with PyTorch
  16. PointNet for 3D Object Classification
  17. The 3D Data Science Workflow
  18. From 3D Generative AI to Spatial AI

Our physical world is grounded in three dimensions. To create technology that can reason about and interact with it…

References

Voxels

3D Geodata Academy

https://learngeodata.eu/3d-newsletter/