Can AI generate 3D models?
The easy-to-use method with trained models, datasets, and code.
3D representation for machine learning has been a long-term issue for computer vision.
Numerous methods have been developed, including signed distance functions, point clouds, and 3D voxels. Their main weakness is a presumption: most representations require a 3D model, necessitating either the production of 3D data using tools like photogrammetry and LiDAR or the payment of an artist to create a 3D model for you. Additionally, it is impossible to scan at scale a variety of objects, including those that are highly reflective, "mesh-like," like shrubs and chain-link fences, or translucent. Finally, the model's accuracy might be affected by stair-stepping effects or drift often caused by 3D reconstruction flaws.
The complete solution described below solves all problems and additionally includes trained models, datasets and code.
See now how it works: