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公开(公告)号：US11080562B1
公开(公告)日：2021-08-03
申请号：US16441167
申请日：2019-06-14
申请人： Shreyas Saxena , Wenda Wang , Guanhang Wu , Nitish Srivastava , Dimitrios Kottas , Cuneyt Oncel Tuzel , Luciano Spinello , Ricardo da Silveira Cabral
发明人： Shreyas Saxena , Wenda Wang , Guanhang Wu , Nitish Srivastava , Dimitrios Kottas , Cuneyt Oncel Tuzel , Luciano Spinello , Ricardo da Silveira Cabral
摘要： A method includes obtaining training samples that include images that depict objects and annotations of annotated key point locations for the objects. The method also includes training a machine learning model to determine estimated key point locations for the objects and key point uncertainty values for the estimated key point locations by minimizing a loss function that is based in part on a key point localization loss value that represents a difference between the annotated key point locations and the estimated key point locations values and is weighted by the key point uncertainty values.
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公开(公告)号：US11282180B1
公开(公告)日：2022-03-22
申请号：US16857221
申请日：2020-04-24
摘要： A method includes determining a detection output that represents an object in a two-dimensional image using a detection model, wherein the detection output includes a shape definition that describes a shape and size of the object; defining a three-dimensional representation based on the shape definition, wherein the three-dimensional representation includes a three-dimensional model that represents the object that is placed in three-dimensional space according to a position and a rotation; determining a three-dimensional detection loss that describes a difference between the three-dimensional representation and three-dimensional sensor information; and updating the detection model based on the three-dimensional detection loss.
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