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   frogzone@wizard.casa's status on Friday, 22-Nov-2024 02:01:18 JST frogzone

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   !!NOT AN ENDORSEMENT!!

   ## From Local Systems to Shared Understanding

   Our current neural map is a viable geospatial model, active and usable right now as part of Niantic’s VPS. It is also most certainly “large”. However, our vision of a “Large Geospatial Model” goes beyond the current system of independent local maps.

   An entirely local model might lack complete coverage of their respective locations. No matter how much data we have available on a global scale, locally, it will often be sparse. The main failure mode of a local model is its inability to extrapolate beyond what it has already seen and from where the model has seen it. Therefore, local models can only position camera views similar to the views they have been trained with already.

   Imagine yourself standing behind a church. Let us assume the closest local model has seen only the front entrance of that church, and thus, it will not be able to tell you where you are. The model has never seen the back of that building. But on a global scale, we have seen a lot of churches, thousands of them, all captured by their respective local models at other places worldwide. No church is the same, but many share common characteristics. An LGM is a way to access that distributed knowledge.

   An LGM distills common information in a global large-scale model that enables communication and data sharing across local models. An LGM would be able to internalize the concept of a church, and, furthermore, how these buildings are commonly structured. Even if, for a specific location, we have only mapped the entrance of a church, an LGM would be able to make an intelligent guess about what the back of the building looks like, based on thousands of churches it has seen before. Therefore, the LGM allows for unprecedented robustness in positioning, even from viewpoints and angles that the VPS has never seen.

   The global model implements a centralized understanding of the world, entirely derived from geospatial and visual data. The LGM extrapolates locally by interpolating globally.

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