Map2Earn: Merging Physical and Virtual Worlds

One method is gaining ground among the various approaches to building the metaverse. That is mapping the physical world to create something like a mirror world and creating AR experiences on top of it. 

The fundamental pieces of a metaverse that merges the physical and virtual worlds are 3D maps generated based on geolocation. To build these maps, we need imagery of the corresponding locations. Different methods are at our disposal to create that imagery.

We can, for instance, use a satellite-based technique like GPS. However, GPS doesn’t work indoors at all, and outdoors it doesn’t provide the high accuracy necessary for most immersive experiences. An AR application is only as accurate as the device’s internal GPS, which is for many smartphones accurate to within a 4.9m radius under the open sky, and the accuracy worsens if there’s a building nearby. 

AR experiences require centimeter-level accuracy. We need other methods to create quality 3D maps. 

What is Map2Earn?

A more plausible approach can be to use videos or images created directly by users’ smartphones and, in turn, reward the users for generating that content. In other words, companies crowdsource map creation, and users map to earn

Consider the popular AR game Pokemon Go where gamers across the world propose physical locations where playing the game could be fun. Gamers then submit images of those sites so they can be used to build the game’s maps. 

OVER – a decentralized AR metaverse infrastructure company – is now spearheading a new earning mechanism based on a similar approach. It’s called Map2Earn, and it rewards users for generating the imagery for creating 3D maps. 

How do users earn with the Map2Earn mechanism?

Users – also known as mappers – scan physical locations with their smartphones to provide the raw data for map building. The app generates 3D maps based on this raw data and mints them as NFTs. In other words, users own the maps. Consequently, they can trade them on the NFT marketplace of OVER or other NFT marketplaces. 

In the future, OVER will initiate an incentivization program to enable direct open-to-buy orders to purchase 3D maps. 

Credit: OVER

How does Map2Earn work with OVER?

The OVER app assists the mapper during the capture process, which takes around 5 minutes to create per a typical OVER land, which is equal to 300 sqm. As a result of this process, the following three assets are created. 

#1 3D point cloud of the location

The capture guide provides the mapper with a precise visual reference of the 3D structure of the physical location where the mapper wants to incorporate the AR experience. 

#2 Observer’s location based on a relocalization algorithm

Based on the 3D point cloud of the location, the OVER app implements a relocalization algorithm with 20cm accuracy to locate the observer of the AR experience. This is necessary for delivering a coherent experience. 

#3 Digital twin of the mapped area

The app can also replicate the simulation of the mapped location based on a neural radiance fields (NeRF) algorithm by creating its digital twin. This feature of the app currently delivers a virtual drone fly-through of the mapped area. In the future, a free exploration of the location will be possible to allow users to experience geolocalized AR content even remotely. 

You can download the beta release of the OVER app here

The bottom line

Map2Earn models can have many positive implications for the whole metaverse ecosystem. It has the potential to support the metaverse projects in the same way Play2Earn and Move2Earn models benefitted blockchain games and fitness applications. 

Users will be able to participate in immersive AR experiences and earn rewards made possible by blockchain technology. At the same time, metaverse companies will improve their applications using high-quality 3D maps their communities generate. 


  • Nagi An

    Nagi An is a content writer who is passionate about NFTs, web3, DAOs, and DeFi. She's covers a variety of topics about NFT fundamentals.

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