Workpiece recognition is critical for switching painting trajectory in a flexible robotic spray-painting production line. However, due to the wide variety of sprayed workpiece sizes and types, as well as the presence of poor surface texture, multiview dissimilar (MVD) components, and comparable parts, it is difficult to effectively and reliably identify sprayed workpieces in the real production line. In this study, a recognition approach is proposed based on two-dimensional (2D) instance segmentation and three-dimensional feature pairing. Specifically, the high efficiency of the Mask R-CNN learning model was used for 2D workpiece segmentation and coarse recognition based on small sample training; this was followed by the integration of the fast point feature histogram (FPFH) feature for fine recognition, with its strong discrimination of local details for accurately recognizing MVD and similar-topology workpieces. During the fine recognition stage, the intrinsic shape signature method was used as the key point of the workpiece and vectored using the FPFH feature. The extracted feature was then coarsely paired and verified with topological structure consistency and spatial transformation to obtain the paring rate, which was used as the evaluation criterion to recognize the workpiece. In the experiment, more than 1500 workpieces of 34 categories are used for testing, and the recognition accuracy can reach 99.26% with a running time of less than 1500 ms for a single workpiece.