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๐ Web3 research
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A useful 3D dataset begins with a sharper question than โCan someone scan this?โ
It starts with what, exactly, must be understood about the space.
The requests on @vangrid_ioโs bounty board make the distinction clear. One buyer needs an underground parking bay in Ottawa. Another wants a museum gallery in Glasgow. Recent ones ask for the interior of a Denver coffee shop and the waste-and-returns corner of a supermarket in Cologne.
These are not interchangeable scans. A robotics team may care about clearances, obstacles, and circulation in a parking structure. A retail team may need shelf layouts, counter positions, or back-of-house flow. A public interior and an industrial workspace serve different purposes and demand different spatial detail.
That is why the request itself carries the value. Location is only the entry point. The useful model comes from capturing the right portions of a space with enough context to answer the actual question being asked.
The marketplace is already reflecting this demand: 1,108 bounties and more than $736K in recorded settlements. Physical-world data does not have to be gathered as one enormous, generic map. It can be requested around particular environments and particular needs.
That approach offers a more precise route to the ground truth Physical AI actually requires.
A useful 3D dataset starts with a better question than โCan someone scan this place?โ
It starts with: what exactly needs to be understood about that environment?
Look at the variety of requests appearing on @vangrid_io 's bounty board.
One buyer wants an underground parking bay in Ottawa. Another wants a museum gallery in Glasgow. A recent accepted request asked for the inside of a Denver coffee shop, while another specified a supermarketโs waste and returns corner in Cologne.
These are not interchangeable captures.
A robotics team may care about the layout and obstacles in a parking area. A retail use case may need the arrangement of shelves, counters or back-of-house spaces. A model of a public interior has a different purpose from a model of an industrial workspace.
That is why request-level detail matters. The location is only the starting point. The actual value comes from capturing the right parts of it, with enough spatial context to make the resulting model useful.
@vangrid_io 's marketplace is showing this demand in practice: 1,108 bounties and more than $736K in recorded settlements on the board.
The bigger idea is that physical-world data does not have to be collected as one massive, generic map. It can be requested around specific environments and specific needs.
That is a more interesting path toward building ground truth for Physical AI.