Can AI generate 3D models?

The easy-to-use method with trained models, datasets, and code.

Sep 21, 2022

∙ Paid

Computer vision has had trouble with 3D representation for machine learning for a long time. There have been a lot of different ways to do this, such as signed distance functions, point clouds, and 3D voxels. Their biggest flaw is that they are based on an assumption: most representations need a 3D model. This means that you either have to use tools like photogrammetry and LiDAR to make 3D data or pay an artist to make a 3D model for you. Also, it's not possible to scan objects at a large scale that are highly reflective, "mesh-like," like shrubs and chain-link fences, or transparent. Lastly, stair-stepping effects or drift, which are often caused by mistakes in 3D reconstruction, could make the model less accurate. All problems are solved by the full solution described below, which also includes trained models, datasets, and code. Now, here's how it works: — How do you make a 3D model?

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:

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Can AI generate 3D models?

The easy-to-use method with trained models, datasets, and code.

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