To improve target recognition accuracy and pose estimation precision in unstructured environment, a novel method for automatic recognition and 3D pose estimation of different kinds of targets is presented based on the object CAD model and Kinect V2 RGB-D sensor. The depth image of object CAD model is obtained by a virtual camera, and it is converted into one point cloud. A voxel grid filter is utilized to reduce the number of points for the point cloud of the scene, and the point pair feature(PPF) is used as the global descriptor of CAD model. The similar PPFs are classified as the same group and put into one hash table to recognize and locate the targets. The hash tables of all targets compose the 3D model database. A voting scheme is adopted for different kinds of objects recognition and 3D pose estimation. Pose cluster and ICP registration are employed to refine 3D pose. The accuracy of 3D pose estimation is improved by filtering the mismatching poses. The recognition rate and the pose estimation error of three kinds of pipe joints are analyzed in the environment of virtual robot experimentation platform (V-REP). The simulation results show that the average recognition rate of three kinds of pipe joints is 96%. The position error is less than 4 mm and the orientation error is no more than 2°, which meet the requirement of manipulator grasping. The experiments of comparison with other mainstream methods are performed. The experimental results show that the recognition rate and the F1 score of the proposed method are superior to those of two other methods.