Digital twin : A set of data relating to a subject to be preserved, aggregated by AI into a 3D scene (an expert model). This is a dynamic set (evolving over time) of heterogeneous content (images, archives, reference 3D models, exports, acoustic map, semantic slicing, etc.) assembled in such a way as to be interpreted and used by other AIs, and to enable the generation of exports.

Export : A selection of data (e.g. a 3D model) optimized for a specific use (AR / VR / Unity / Unreal / Voxel, etc.). These exports can concern the entire subject or a specific part of it (work of art, architectural element, texture, etc.) and can be distributed on our platform and on various asset stores.

• To equip researchers and architects in their preservation and renovation missions.

• To visit a monument remotly or beyond the limits of time.

• To serve as virtual studio sets or special effects for the creation of films, TV programs, advertisements or virtual events.

• To anchor video games environment or metaverse in a specific context.

• To create material reproductions (e.g. a 3D printing) of a subject or parts of it.

• To support new forms of digital and immersive art.

• To support all services located indoors or outdoors (with or without GPS signal).
• To be embedded in a third party Gen AI to be use to generate results (text, images, videos, etc.)

And the best use cases have yet to be invented…

Enhanced archive

To keep a rich record of what is valuable and fragile by aggregating a large smart set of data on a specific heritage element.

Augmented mediation

To give the public a richer, more diversified, more personalized and more sustainable access to knowledge on a subject.

Economic model

To create a virtuous economic model specific to the twin, allowing to finance the preservation of the real heritage.

Support creation

To provide operational means for creators to reinterpret reality and history in new creative environments (VR, AR, Virtual Studios, metavers, etc.).

Emerging innovation

To propose a bottom-up innovation model to heritage institutions that creates a bridge between creators and them.

New environments of creation (XR) represent huge opportunities to invent new ways of promoting and broadcasting culture BUT production of a digital twin remains a very manual work process, very long, expensive, not very resilient (one capture for one output) and only based on visual perception.

In 2021, the European Union asked its member states to create 16 million digital twins of monuments by 2030. How to make this recommendation realistic without impoverishing it ? Lay3rs collaborates with research laboratories (CEA, ULB, Ecole Polytechnique) to develop new solutions to make this process more intelligent and able to fully exploit all existing data.

The use of AI to aggregate existing data and extrapolate missing data radically changes the way digital twins are produced. Where previously it was necessary to make in-situ captures for each new model, it is now possible to aggregate all types of data into a single 3D scene, recycle all existing data and generate the missing data, all for a realistic result. In the Build3rs case, we use computer vision (SLAM) and artificial intelligence (NeRF & Gaussian Splatting) algorithms.

In addition to their use for the renovation or scientific study of a subject, when contributors invest in the creation of a twin, in the acquisition of new data, part of these funds are donated to the institution in charge of preserving the real subject. In time, the portion allocated to them by the DAO from the sale of licenses to exploit the exports will constitute a new channel of regular income for the latter.