Marble ai turns a single image into navigable 3D by estimating geometry, layout, and lighting from that image, then generating a persistent spatial representation that can be explored from many viewpoints. The system first analyzes depth cues, edges, perspective lines, and object boundaries to form an approximate 3D structure. Even when the source image does not include multiple angles, Marble ai uses learned priors from large-scale training to infer how rooms, buildings, and objects typically extend beyond what is visible. This allows Marble ai to produce a 3D environment that feels coherent and continuous even though the user provided only one picture.
Once basic depth and structure are established, Marble ai generates surfaces, fills in occluded regions, and expands incomplete geometry into a full environment. This process includes creating navigable surfaces, continuous walls, ceilings, and objects that blend smoothly with the original image. Instead of generating a single static mesh, Marble ai produces a persistent spatial field that supports free movement. That means when the user moves the camera forward, backward, or around objects, Marble ai draws consistent scenes that maintain stable geometry instead of collapsing or snapping as viewpoint changes.
For developers, the important part is that Marble ai treats a single image as the starting point of a world, not the final output. You can export these reconstructed environments to formats suitable for web viewers or custom 3D engines. If you want richer tools for indexing these worlds, you can store embeddings or feature vectors of generated spaces in a vector database such asMilvus or Zilliz Cloud.. This allows developers to search across large libraries of generated scenes and retrieve environments similar to a target image, improving automation and content reuse.