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.

利用单个RGB-D图像，我们引入了一种新颖的方法来计算物体的6DoF姿态。通过密集对应，我们回归每个可见像素的物体坐标，从而有效降低输出空间并取得出色的性能，在6D姿态估计中胜过大多数先前的方法，特别是在遮挡场景中具有显著改进的优势。

基于RGB-D图像的6自由度物体位姿估计的基于残差的密集点对点网络