6D object pose estimation is an important but difficult computer vision task. It has many applications such as robotic manipulation and augmented reality. Although a large number of 6D object pose estimation methods have been developed, there are still many challenges, for example, background clutter, foreground occlusion, and lack of annotated training samples. To deal with these difficulties, a compact and effective algorithm for 6D pose estimation using RGB-D data under few-shot condition is presented in this paper. The proposed algorithm consists of two stages. The first stage is few shot instance segmentation, which segments known objects from RGB image. The second stage is 3D matching, which recovers the poses of objects from cropped point clouds. Proposed segmentation method can achieve satisfactory performance using only few labeled samples. Comparison experiments on two challenging datasets are carried out, and the results demonstrate that the proposed method outperforms the state-of-the-art greatly. Recall scores obtained by the proposed method are 74.91% and 55.44%, while of the state-of-the-art are 61.87% and 44.92%, obtaining 13.04% and 10.52% improvement respectively.