Systems and methods for 3D image distification
摘要
說(shuō)明書
As described herein, a computing device may provide 3D image Distification by first obtaining a three dimensional (3D) image that includes rules defining a 3D point cloud. The computing device may then generate a two dimensional (2D) image matrix based upon the 3D image. The 2D image matrix may include 2D matrix point(s) mapped to the 3D image. Each 2D matrix point can be associated with a horizontal coordinate and a vertical coordinate. The computing device can generate an output feature vector that includes, for at least one of the 2D matrix points, the horizontal coordinate and the vertical coordinate of the 2D matrix point, and a depth coordinate of a 3D point in the 3D point cloud of the 3D image. The 3D point can have a nearest horizontal and vertical coordinate pair that corresponds to the horizontal and vertical coordinates of the at least one 2D matrix point.
In some embodiments, the output feature vector may indicate one or more image feature values associated with the 3D point. The feature values can define one or more items of interest in the 3D image. The items of interest in the 3D image can include, for example, a person's head, a person's facial features, a person's hand, or a person's leg. In some aspects, the output feature vector is input into a predictive model for making predictions with respect to the items of interest.
In some embodiments, the output feature vector can further include a distance value generated based on the distance from the at least one 2D matrix point to the 3D point. In other embodiments, a total quantity of the 2D matrix points mapped to the 3D image can be less (i.e., to create a courser granularity) than a total quantity of horizontal and vertical coordinate pairs for all 3D points in the 3D point cloud of the 3D image.
