首页> 外国专利> EMPLOYING THREE-DIMENSIONAL (3D) DATA PREDICTED FROM TWO-DIMENSIONAL (2D) IMAGES USING NEURAL NETWORKS FOR 3D MODELING APPLICATIONS AND OTHER APPLICATIONS

EMPLOYING THREE-DIMENSIONAL (3D) DATA PREDICTED FROM TWO-DIMENSIONAL (2D) IMAGES USING NEURAL NETWORKS FOR 3D MODELING APPLICATIONS AND OTHER APPLICATIONS

机译:使用神经网络从二维(2D)图像中预测的三维(3D)数据用于3D建模应用程序和其他应用程序

摘要

The disclosed subject matter is directed to employing machine learning models configured to predict 3D data from 2D images using deep learning techniques to derive 3D data for the 2D images. In some embodiments, a method is provided that comprises receiving, by a system comprising a processor, a panoramic image, and employing, by the system, a three-dimensional data from two-dimensional data (3D-from-2D) convolutional neural network model to derive three-dimensional data from the panoramic image, wherein the 3D-from-2D convolutional neural network model employs convolutional layers that wrap around the panoramic image as projected on a two-dimensional plane to facilitate deriving the three-dimensional data.
机译:所公开的主题涉及采用机器学习模型,该机器学习模型被配置为使用深度学习技术从2D图像预测3D数据以导出2D图像的3D数据。在一些实施例中,提供了一种方法,该方法包括:由包括处理器的系统接收全景图像,并且由所述系统采用来自二维数据(3D-from-2D)的卷积神经网络的三维数据。三维模型从全景图像中导出三维数据,其中3D自2D卷积神经网络模型采用卷积层,该层围绕投影在二维平面上的全景图像进行包裹,以方便导出三维数据。

著录项

  • 公开/公告号US2019026957A1

    专利类型

  • 公开/公告日2019-01-24

    原文格式PDF

  • 申请/专利权人 MATTERPORT INC.;

    申请/专利号US201816141630

  • 发明设计人 DAVID ALAN GAUSEBECK;

    申请日2018-09-25

  • 分类号G06T19/20;H04N13/10;H04N13/156;H04N13/204;H04N13/106;H04N13/246;

  • 国家 US

  • 入库时间 2022-08-21 12:05:54

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