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!!NOT AN ENDORSEMENT!!

## Our work so far

Over the past five years, Niantic has focused on building our Visual Positioning System (VPS), which uses a single image from a phone to determine its position and orientation using a 3D map built from people scanning interesting locations in our games and Scaniverse.

With VPS, users can position themselves in the world with centimeter-level accuracy. That means they can see digital content placed against the physical environment precisely and realistically. This content is persistent in that it stays in a location after you’ve left, and it’s then shareable with others. For example, we recently started rolling out an experimental feature in Pokémon GO, called Pokémon Playgrounds, where the user can place Pokémon at a specific location, and they will remain there for others to see and interact with.

Niantic’s VPS is built from user scans, taken from different perspectives and at various times of day, at many times during the years, and with positioning information attached, creating a highly detailed understanding of the world. This data is unique because it is taken from a pedestrian perspective and includes places inaccessible to cars.

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Today we have 10 million scanned locations around the world, and over 1 million of those are activated and available for use with our VPS service. We receive about 1 million fresh scans each week, each containing hundreds of discrete images.

As part of the VPS, we build classical 3D vision maps using structure from motion techniques - but also a new type of neural map for each place. These neural models, based on our research papers ACE (2023) and ACE Zero (2024) do not represent locations using classical 3D data structures anymore, but encode them implicitly in the learnable parameters of a neural network. These networks can swiftly compress thousands of mapping images into a lean, neural representation. Given a new query image, they offer precise positioning for that location with centimeter-level accuracy.

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Niantic has trained more than 50 million neural nets to date, where multiple networks can contribute to a single location. All these networks combined comprise over 150 trillion parameters optimized using machine learning.

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