In this paper, we propose a novel Grasp Pose Domain Adaptation Network (GPDAN) to achieve sim-to-real domain adaptation for 6-DoF grasp pose detection. The main task of GPDAN is to detect feasible 6-DoF grasp poses in cluttered scenes. A point-wise self-supervised domain classification module with point cloud mixture and feature fusion strategy is proposed as the auxiliary task to promote the feature alignment between the source and target domain through adversarial training. Experimental results on both simulation and real-world environments demonstrate that GPDAN outperforms other approaches in detecting 6-DoF grasps on the target domain, highlighting the effectiveness of GPDAN in improving the performance of 6-DoF grasp pose detectors trained in simulation and deployed in real-world environments without any further laborious labeling.