Python Point Clouds: Scene Graphs for LLM Reasoning (Tutorial Part 1)

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Dive deep into the world of 3D data science! This tutorial shows you how to create a scene graph from a point cloud using Python, focusing on semantic segmentation, object detection, spatial relationships, and exporting in OpenUSD format.

Discover how Large Language Models (LLMs) like Google Gemini can leverage this spatial data for advanced reasoning and decision-making. Learn the power of 3D data science, moving beyond simple geometry to create intelligent solutions for real-world problems, like optimizing classroom layouts.

Dive deep into the world of 3D data science! This tutorial shows you how to create a scene graph from a point cloud using Python, focusing on semantic segmentation, object detection, spatial relationships, and exporting in OpenUSD format. Discover how Large Language Models (LLMs) like Google Gemini can leverage this spatial data for advanced reasoning and decision-making. Learn the power of 3D data science, moving beyond simple geometry to create intelligent solutions for real-world problems, like optimizing classroom layouts.

This video is Part 1 of a two-part series where we cover:
- Setting up the Python environment with NumPy, Pandas, NetworkX, scikit-learn, Open3D, and Matplotlib
- Importing and pre-processing data for a semantically labeled point cloud
- Utilizing DBScan clustering for instance segmentation
- Computing geometric features (volume, surface area, compactness, etc.)
- Identifying spatial relationships between objects
- Building the scene graph network
- Visualizing the scene graph in 2D using Matplotlib

Stay tuned for Part 2 where we'll demonstrate exporting the scene graph in OpenUSD format and integrate it with LLMs for spatial intelligence.

⏱️ TIMESTAMPS:

[00:00] Introduction: The Power of Python and Scene Graphs
[01:00] Minimal Setup: Essential Python Libraries
[02:00] Importing OpenUSD for 3D Export
[02:40] Data Loading and Preprocessing: Semantic Labeling
[04:20] Visualizing the Semantic Cloud: Adding Color Labels
[06:00] Leveraging NetworkX for Graph Structures
[07:30] Semantic Analysis & Object Detection with DBScan
[10:40] Computing Geometric Features: Volume, Surface Area, Compactness
[13:40] Computing Spatial Relationships: Defining Topology
[15:00] Creating a Scene Graph
[17:00] Preview of Part 2

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[nickbaltsavias]

Helloo, any chance for accessing the demo point cloud?

[jwlee]

First! I just bought your book :) Thank you!