Invention Application
- Patent Title: TECHNIQUES FOR TRAINING A MACHINE LEARNING MODEL TO MODIFY PORTIONS OF SHAPES WHEN GENERATING DESIGNS FOR THREE-DIMENSIONAL OBJECTS
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Application No.: US17083153Application Date: 2020-10-28
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Publication No.: US20220130127A1Publication Date: 2022-04-28
- Inventor: Ran ZHANG , Morgan FABIAN , Ebot NDIP-AGBOR , Lee Morris TAYLOR
- Applicant: AUTODESK, INC.
- Applicant Address: US CA San Rafael
- Assignee: AUTODESK, INC.
- Current Assignee: AUTODESK, INC.
- Current Assignee Address: US CA San Rafael
- Main IPC: G06T19/20
- IPC: G06T19/20 ; G06N3/08 ; G06T3/40 ; G06T17/20

Abstract:
In various embodiments, a training application trains a machine learning model to modify portions of shapes when designing 3D objects. The training application converts first structural analysis data having a first resolution to first coarse structural analysis data having a second resolution that is lower than the first resolution. Subsequently, the training application generates one or more training sets based on a first shape, the first coarse structural analysis data, and a second shape that is derived from the first shape. Each training set is associated with a different portion of the first shape. The training application then performs one or more machine learning operations on the machine learning model using the training set(s) to generate a trained machine learning model. The trained machine learning model modifies at least a portion of a shape having the first resolution based on coarse structural analysis data having the second resolution.
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