This paper presents an estimation algorithm for the six degree-of-freedom camera pose obtained from a single RGB image in a specific environment using a combination of the known image and point cloud information. Specifically， we propose a multi-stage camera pose estimation algorithm based on dense scene regression. First， the camera pose estimation dataset is composed by combining the depth image information and Structure from Motion （SFM） algorithm. Then， for the first time， we introduce depth image retrieval into the construction of two- and three-dimensional （2D-3D） matching points. Using the proposed pose optimization function， a multi-stage camera pose estimation method is proposed. The ResNet network considerably improves the pose estimation accuracy. Experimental results indicate that the pose estimation accuracy is 82.7% on average in the open dataset 7 scenes， and 94.8% in our own dataset （estimated poses falling within the threshold of 5 cm/5°）. Compared with other camera pose estimation methods， our method has better pose estimation accuracy for both our and public datasets.