Gm fam
One detail about
@vangrid_io that is easy to miss is what happens after the phone recording
The requester is not simply paying somebody to send over a random video file
An accepted capture can go through reconstruction and become a textured 3D mesh in GLB format
That changes the usefulness of the network
Video is great for humans because our brains automatically understand geometry
We can look at footage and infer walls, distance, obstacles, openings and surfaces
Machines need that information represented much more explicitly
A reconstructed spatial model gives software something closer to the structure of the environment itself
- Now imagine that workflow at scale
- A robotics company needs a location
- Someone nearby captures it
- The capture receives a provenance record
- - The requester accepts the delivery
The scene is reconstructed
The resulting asset can then enter simulation, mapping, planning or another Physical AI workflow
That is a more interesting product than “get paid to take videos.”
The video is just the acquisition format
The real product is structured spatial information about a place that did not previously exist inside the buyer's system
And because the capture can be commissioned again later, the model does not necessarily have to represent the location forever
Physical environments have versions
A good spatial-data network should be able to create the next version when reality changes