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.

翻译：本文提出了一种新颖方法，利用单张RGB-D图像计算物体的六自由度姿态。与现有直接预测物体姿态或依赖稀疏关键点进行姿态恢复的方法不同，我们采用密集对应策略解决这一挑战性任务——即为每个可见像素回归物体坐标。该方法利用现有的物体检测技术，引入重投影机制调整相机内参矩阵以适应RGB-D图像的裁剪操作。此外，我们将三维物体坐标转换为残差表示，有效缩小输出空间并提升性能。通过大量实验验证了该方法在六自由度姿态估计中的有效性，尤其在遮挡场景下优于多数先前方法，并显著超越了现有最先进技术。代码已开源：https://github.com/AI-Application-and-Integration-Lab/RDPN6D。