In this work, we present a novel method for determining the 6DoF pose of an object from a single RGB-D image. Unlike existing methods that either directly predict the object’s pose or rely on sparse keypoints for pose recovery, our approach addresses this challenging task using dense correspondence, i.e., it regresses the object coordinates for each visible pixel. Our approach leverages readily available object detection methods. A re-projection mechanism is introduced to change the camera intrinsic matrix to handle cropping in RGB-D images. Moreover, we transform the 3D object coordinates into a residual representation, which proves effective in reducing the output space and yields superior performance. We conducted extensive experiments to validate the effectiveness of our approach for 6D pose estimation. Our approach outperforms most previous methods, especially in occlusion scenarios, and demonstrates notable improvements over the state-of-the-art methods.