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Build 3D Scene Graphs for Spatial AI LLMs from Point Cloud (Python Tutorial)
Transform semantic point clouds into intelligent OpenUSD scene graphs with spatial relationships. Complete Python tutorial with NetworkX and LLM integration.
The hardest part of spatial AI isn’t the algorithms.
It’s making 3D data understandable for you, me, and the people who could really benefit from the knowledge (perhaps one of them is the grandmother next door).
When prototyping with Python, we geekily get satisfaction from visualizing colored point clouds. It is a nice sight!
But it may not be very useful for others or decision-making applications.
Indeed, your 3D data contains relationships, hierarchies, and semantic meaning that the point cloud formats can’t capture.
And we need to leverage that to open the door to queryable spatial intelligence.
To enable autonomous systems to understand 3D environments like humans do.
And this is why we propose exploring an innovative idea:
What if we simply recognized each “object” in the point cloud, labeled it with a…
