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1.发明申请Systems and Methods for Scale Invariant 3D Object Detection Leveraging Processor Architecture 有权用于尺度不变的3D对象检测的系统和方法利用处理器架构公开(公告)号：US20160335496A1
公开(公告)日：2016-11-17
申请号：US15219798
申请日：2016-07-26
Applicant: Google Inc.
Inventor： Stefan Hinterstoisser
CPC classification number: G06K9/00664 , B25J5/007 , B25J9/0093 , B25J13/089 , B25J19/023 , G06K9/00201 , G06K9/00208 , G06K9/4671 , G06K9/6215 , G06K9/66 , G06K2009/4666 , G06K2209/29 , Y10S901/01 , Y10S901/09 , Y10S901/47
Abstract: An example method includes receiving a plurality of templates of a plurality of objects, where a template comprises feature values sampled at corresponding points of a two-dimensional grid of points positioned over a particular view of an object and scaled based on a depth of the object at the particular view. The method may further include receiving an image of an environment and determining a matrix representative of the image, where a row of the matrix comprises feature values sampled at a particular point of the two-dimensional grid positioned over one or more locations within the image and scaled based on depths of the one or more locations. The method may additionally include determining at least one similarity vector corresponding to at least one template and using the at least one similarity vector to identify at least one matching template for at least one object located within the image.
Abstract translation: 示例性方法包括接收多个对象的多个模板，其中模板包括在位于对象的特定视图上的点的二维网格的对应点处采样的特征值，并且基于对象的深度 在特定的观点。 该方法还可以包括接收环境的图像并确定表示图像的矩阵，其中矩阵行包括在位于图像内的一个或多个位置的二维网格的特定点处采样的特征值，以及 基于一个或多个位置的深度进行缩放。 该方法可以另外包括确定与至少一个模板相对应的至少一个相似度向量，并且使用至少一个相似性向量来识别位于图像内的至少一个对象的至少一个匹配模板。
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2.发明授权Systems and methods for scale invariant 3D object detection leveraging processor architecture 有权用于尺度不变的3D对象检测的系统和方法利用处理器架构公开(公告)号：US09424470B1
公开(公告)日：2016-08-23
申请号：US14466379
申请日：2014-08-22
Applicant: Google Inc.
Inventor： Stefan Hinterstoisser
CPC classification number: G06K9/00664 , B25J5/007 , B25J9/0093 , B25J13/089 , B25J19/023 , G06K9/00201 , G06K9/00208 , G06K9/4671 , G06K9/6215 , G06K9/66 , G06K2009/4666 , G06K2209/29 , Y10S901/01 , Y10S901/09 , Y10S901/47
Abstract: An example method includes receiving a plurality of templates of a plurality of objects, where a template comprises feature values sampled at corresponding points of a two-dimensional grid of points positioned over a particular view of an object and scaled based on a depth of the object at the particular view. The method may further include receiving an image of an environment and determining a matrix representative of the image, where a row of the matrix comprises feature values sampled at a particular point of the two-dimensional grid positioned over one or more locations within the image and scaled based on depths of the one or more locations. The method may additionally include determining at least one similarity vector corresponding to at least one template and using the at least one similarity vector to identify at least one matching template for at least one object located within the image.
Abstract translation: 示例性方法包括接收多个对象的多个模板，其中模板包括在位于对象的特定视图上的点的二维网格的对应点处采样的特征值，并且基于对象的深度 在特定的观点。 该方法还可以包括接收环境的图像并确定表示图像的矩阵，其中矩阵行包括在位于图像内的一个或多个位置的二维网格的特定点处采样的特征值，以及 基于一个或多个位置的深度进行缩放。 该方法可以另外包括确定与至少一个模板相对应的至少一个相似度向量，并且使用至少一个相似性向量来识别位于图像内的至少一个对象的至少一个匹配模板。
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公开(公告)号：US09327406B1
公开(公告)日：2016-05-03
申请号：US14463156
申请日：2014-08-19
Applicant: Google Inc.
Inventor： Stefan Hinterstoisser , Kurt Konolige
CPC classification number: B25J9/1697 , G05B2219/31066 , G05B2219/40006 , Y02P90/04
Abstract: One or more images of a physical environment may be received, where the one or more images may include one or more objects. A type of surface feature predicted to be contained on a portion of one or more surfaces of a single object may be determined. Surface features of the type within regions of the one or more images may then be identified. The regions may then be associated to corresponding objects in the physical environment based on the identified surface features. Based at least in part on the regions associated to the corresponding objects, a virtual representation of the physical environment may be determined, the representation including at least one distinct object segmented from a remaining portion of the physical environment so as to virtually distinguish a boundary of the at least one distinct object from boundaries of objects present in the remaining portion of the physical environment.
Abstract translation: 可以接收物理环境的一个或多个图像，其中一个或多个图像可以包括一个或多个对象。 可以确定预测包含在单个物体的一个或多个表面的一部分上的一种表面特征。 然后可以识别一个或多个图像的区域内的类型的表面特征。 然后可以基于所识别的表面特征将区域与物理环境中的相应对象相关联。 至少部分地基于与相应对象相关联的区域，可以确定物理环境的虚拟表示，所述表示包括从物理环境的剩余部分分割的至少一个不同对象，以便虚拟地区分 所述至少一个不同对象来自存在于物理环境的剩余部分中的对象的边界。
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