Instance segmentation of 3D point clouds is a critical preprocessing step in industrial automation. However, there are often many occlusions in industrial grasping scenarios, which makes it difficult for instance segmentation networks of 3D point clouds to distinguish between similar objects. To this end, this study proposes an improved algorithm based on FPCC. This algorithm has two branches, including a center point branch for inferring the center points of instances and an embedded feature branch for describing point features. The segmentation results are obtained by clustering algorithms. The feature enhancement (FEH) module plays a crucial role in improving the accuracy of center point prediction. This module employs FEH methods to improve the prediction accuracy and further modifies the loss function for center point prediction. Experimental results show that compared with the FPCC algorithm, the improved algorithm increases the Precision and Recall values by 10% and 15% respectively.