Selective multi-view deep model for 3d object classification

Abstract

A selective multi-view method and a 3D object recognition subsystem for 3D object classification. The method includes inputting, by a 3D imaging sensor, a 3D data representation of a 3D object, extracting, by processing circuitry, multiple view images from the 3D data representation of the 3D object, selecting, by the processing circuitry, a most influential view based on an assignment of importance scores using a Cosine similarity method between visual features detected by at least one pre-trained convolutional neural network (CNN). The method further includes predicting, by the processing circuitry, a classification of the 3D object based on the selected most influential view and outputting, by the processing circuitry, a class of the 3D object. The subsystem may be implemented for a robotic pick and place manipulator.

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