Nvidia patents method to build 3D objects from compressed data
Nvidia describes a system that creates 3D objects by learning from compressed data and a table of hidden features. It encodes the object's shape into a multi-layered grid, allowing a computer model to reconstruct the 3D form from these stored details.
In the filing’s own words
Approaches presented herein provide systems and methods for generating three-dimensional (3D) objects using compressed data as an input. One or more models may learn from a hash table of latent features to map different features to a reconstruction domain, using a hash function as part of a learned process. A 3D shape for an object may be encoded to a multi-layered grid and represented by a series of embeddings, where given point within the grid May be interpolated based on the embeddings for a given layer of the multi-layered grid. A decoder may then be trained to use the embeddings to generate an output object.
What exactly they claim (claim 1)
1. A processor, comprising: one or more circuits to encode a three-dimensional (3D) object as a plurality of two-dimensional (2D) projections over a plurality of levels of detail, wherein each 2D projection encodes 3D coordinates of the 3D object at a level of detail, of the plurality of levels of detail, corresponding to the respective 2D projection.
Facts
- Company
- Nvidia (Nvidia Corporation)
- Official title
- GENERATIVE MODELING FOR 3D OBJECTS USING NEURAL IMPLICIT REPRESENTATIONS
- Publication number
- US20260301322A1
- Application number
- 19702871
- Filed
- Jun 9, 2026
- Published
- Oct 1, 2026
- Inventors
- 8
- Claims
- 20
Classified under
G06T 17/20 Physics & computingG06T 3/4007 Physics & computingG06T 2210/36 Physics & computingMore from Nvidia in AI & software
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Summaries are simplified from the filing’s own abstract and first claim. A patent filing is not a product announcement.
Patent data and drawings: U.S. Patent and Trademark Office (USPTO). Nebula is not affiliated with or endorsed by the USPTO.
