This paper addresses the challenging task of determining the position and posture of small-scale thin metal parts with multi-objective overlapping. To tackle this problem, we propose a method that utilizes instance segmentation and a three-dimensional (3D) point cloud for recognizing the posture of thin special-shaped metal parts. We investigate the process of obtaining a single target point cloud by aligning the target mask with the depth map. Additionally, we explore a pose estimation method that involves registering the target point cloud with the model point cloud, designing a registration algorithm that combines the sample consensus initial alignment algorithm (SAC-IA) for coarse registration and the iterative closest point (ICP) algorithm for fine registration. The experimental results demonstrate the effectiveness of our approach. The average accuracy of the instance segmentation models, utilizing ResNet50 + FPN and ResNet101 + FPN as backbone networks, exceeds 97%. The time consumption of the ResNet50 + FPN model is reduced by 50%. Furthermore, the registration algorithm, which combines the SAC-IA and ICP, achieves a lower average consumption time while satisfying the requirements for the manufacturing of new energy batteries.