We explore how a visual signal can become a structured understanding of depth, geometry, material, and scale. Our own neural reconstruction models are a long-term goal, not part of the current MVP.
A photograph records light, surface, and a chosen angle. It also suggests a world beyond its edges: volume, distance, and the relationships that make an object coherent.
Our work begins with the asset already in circulation. We ask what it would take for a system to read that signal as the beginning of a spatial model.
3DNA is the ingestion layer of PhantomView, in development. It is designed to turn product photos into 3D models using existing generative models. Training our own neural reconstruction models is a long-term research goal, not part of the MVP.
The goal is a more useful dimensional foundation: an asset that can be inspected, versioned, and prepared for the next interface.
A spatial result matters when it can move into a useful experience. See how Immersive Frame turns dimensional assets into lighter interactive delivery.
Explore Immersive Frame