  In this work, we introduce a novel method for calculating the 6DoF pose of an
object using a single RGB-D image. Unlike existing methods that either directly
predict objects' poses or rely on sparse keypoints for pose recovery, our
approach addresses this challenging task using dense correspondence, i.e., we
regress the object coordinates for each visible pixel. Our method leverages
existing object detection methods. We incorporate a re-projection mechanism to
adjust the camera's intrinsic matrix to accommodate cropping in RGB-D images.
Moreover, we transform the 3D object coordinates into a residual
representation, which can effectively reduce the output space and yield
superior performance. We conducted extensive experiments to validate the
efficacy 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. Our code is available on
https://github.com/AI-Application-and-Integration-Lab/RDPN6D.
