To address the difficulties in object recognition caused by noise, occlusion, and other factors in Bin-Picking by an industrial robot, a three-dimensional (3D) recognition algorithm using curvature point pair features is proposed. Based on the original point pair feature, a curvature difference feature is introduced to make the point pair more descriptive and improve the point cloud registration rate. In the preprocessing stage, a watershed algorithm based on distance transformation is used to segment the scene point cloud, extract candidate targets, and accelerate the algorithm matching. Furthermore, a new weighted voting scheme is proposed for the pose voting stage, and it assigns a larger weight to stronger point pairs based on the curvature difference information and further improves the point cloud registration rate. The experimental results show that the proposed algorithm significantly improves the accuracy and speed compared to the original algorithm, and it can meet the requirements of practical application scenarios.