- Patent Title: Learning a neural network for inference of editable feature trees
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Application No.: US16727124Application Date: 2019-12-26
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Publication No.: US11436795B2Publication Date: 2022-09-06
- Inventor: Eloi Mehr , Fernando Manuel Sanchez Bermudez
- Applicant: DASSAULT SYSTEMES
- Applicant Address: FR Velizy Villacoublay
- Assignee: DASSAULT SYSTEMES
- Current Assignee: DASSAULT SYSTEMES
- Current Assignee Address: FR Velizy Villacoublay
- Agency: Oblon, McClelland, Maier & Neustadt, L.L.P.
- Priority: EP18306884 20181229
- Main IPC: G06T17/00
- IPC: G06T17/00 ; G06N3/04 ; G06N3/08 ; G06N3/00 ; G06T17/10 ; G06F3/0484 ; G06N5/04

Abstract:
The disclosure notably relates to a computer-implemented method for learning a neural network configured for inference, from a discrete geometrical representation of a 3D shape, of an editable feature tree representing the 3D shape. The editable feature tree includes a tree arrangement of geometrical operations applied to leaf geometrical shapes. The method includes obtaining a dataset including discrete geometrical representations each of a respective 3D shape, and obtaining a candidate set of leaf geometrical shapes. The method also includes learning the neural network based on the dataset and on the candidate set. The candidate set includes at least one continuous subset of leaf geometrical shapes. The method forms an improved solution for digitization.
Public/Granted literature
- US20200211276A1 LEARNING A NEURAL NETWORK FOR INFERENCE OF EDITABLE FEATURE TREES Public/Granted day:2020-07-02
Information query
IPC分类:
G | 物理 |
G06 | 计算;推算或计数 |
G06T | 一般的图像数据处理或产生 |
G06T17/00 | 用于计算机制图的3D建模 |