In traditional industrial fields, a robot arm is usually used for high-precision or highly repetitive movements, but now, with the development of three-dimensional (3D) stereo machine vision in smart manufacturing, the smart factory has moved toward the development of a robot arm combined with the image recognition technology. Currently, in the manufacturing industry, most of the images for computing are obtained using two-dimensional (2D) machine vision; here, the 2D advantage is that the camera lens can obtain the simulation of the plane color pixel, but the disadvantage is that it cannot obtain the real space depth distance information, resulting in a more accurate analysis of the workpiece position and features. Therefore, in this study, a pixel-wise voting network (PVNet)-based object pose estimation and feature extraction was developed to perform more diverse object testing for the considered network model. Unlike other workpiece picking systems for smart manufacturing, most of the systems today still framed the workpiece in two dimensions only, but the approach proposed in this paper framed the workpiece pose in three dimensions. Thus, the network could successfully predict the pose even when the workpiece was obscured or the image was not fully captured. The images were input into the neural network by means of supervised learning, and training was performed using transformation matrices between multi-angle images of the artifacts and feature points extracted from the 3D models. The results of this study revealed the pose estimation results of various objects at different viewing angles and proposed the feature gripping strategy for the robot arm to follow this process in the future.

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