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GENERATING SYNTHETIC DIGITAL ASSETS FOR A VIRTUAL SCENE INCLUDING A MODEL OF A REAL-WORLD OBJECT

机译:虚拟场景的合成数字资产的生成,其中包括实物模型

摘要

The disclosure herein describes training a machine learning model to recognize a real-world object based on generated virtual scene variations associated with a model of the real-world object. A digitized three-dimensional (3D) model representing the real-world object is obtained and a virtual scene is built around the 3D model. A plurality of virtual scene variations is generated by varying one or more characteristics. Each virtual scene variation is generated to include a label identifying the 3D model in the virtual scene variation. A machine learning model may be trained based on the plurality of virtual scene variations. The use of generated digital assets to train the machine learning model greatly decreases the time and cost requirements of creating training assets and provides training quality benefits based on the quantity and quality of variations that may be generated, as well as the completeness of information included in each generated digital asset.
机译:本文中的公开内容描述了基于与现实世界对象的模型相关联的生成的虚拟场景变化来训练机器学习模型以识别现实世界对象。获得表示真实世界对象的数字化三维(3D)模型,并围绕3D模型构建虚拟场景。通过改变一个或多个特征来产生多个虚拟场景变化。生成每个虚拟场景变体以包括在虚拟场景变体中标识3D模型的标签。可以基于多个虚拟场景变化来训练机器学习模型。使用生成的数字资产来训练机器学习模型,大大减少了创建训练资产的时间和成本要求,并基于可能产生的变化的数量和质量以及包含在其中的信息的完整性,提供了训练质量的收益。每个生成的数字资产。

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