Objective Existing six-degree-of-freedom 6-DoF object pose estimation methods mainly focus on dealing with trained objects. Estimating the 6-DoF pose of unknown objects with weak texture details or in unstructured environments with occlusion and complex lighting remains a challenging problem. Methods A novel network for 6-DoF object pose estimation was proposed. First a sufficient number of matching point pairs were obtained from image pairs through local feature matching. Second the depth information of these matching point pairs was read via sensors to generate the corresponding point cloud data which was used as the initial point cloud for subsequent high-precision point cloud registration. Finally through the registration process the rotation matrix and translation vector of the source point cloud relative to the target point cloud were obtained. The matrix and vector represented the 6-DoF pose of the unknown object in the robot coordinate system. Results The proposed network demonstrated strong performance in 6D pose estimation on benchmark datasets with a dADD-S value of 80. 0%. It also achieved a dADD-S value of 78. 0% on the Occlusion Linemod dataset demonstrating outstanding performance. Conclusion This network exhibits good generalization ability. It can accurately estimate the 6-DoF pose of objects under conditions such as severe occlusion background clutter and poor lighting. Moreover it shows good robustness against random noise.