Object pose estimation is the fundamental technology of robot manipulation systems. Recently, various learning-based monocular pose estimation methods have achieved outstanding performance by establishing sparse/dense 2D–3D correspondences. However, in cluttered environments, occlusion has been a challenging problem for pose estimation due to limited information provided by visible parts. In this work, we propose an efficient occlusion-aware monocular pose estimation method, called OA-Pose, to learn geometric feature information of occluded objects from cluttered scenes. Our framework takes RGB images as input and generates 2D–3D correspondences of visible and invisible parts based on the proposed Occlusion-aware Geometry Alignment Module. Extensive experiments show that our method is superior and competitive with state-of-the-art on multiple public datasets. We also conduct grasping experiments with different degrees of object occlusion, demonstrating the usability of our algorithm to deploy on robots in unstructured environments.