Keypoint-Based Disentangled Pose Network for Category-Level 6-D Object Pose Tracking

Category-level 6-D object pose tracking is very challenging in the field of 3-D computer vision. Keypoint-based object pose estimation has demonstrated its effectiveness in dealing with it. However, current approaches first estimate the keypoints through a neural network and further compute the interframe pose change via least-squares optimization. They estimate rotation and translation in the same way, ignoring the differences between them. In this work, we propose a keypoint-based disentangled pose network, which disentangles the 6-D object pose change to 3-D rotation and 3-D translation. Specifically, the translation is directly estimated by the network and the rotation is indirectly calculated by singular value decomposition according to the keypoints. Extensive experiments on the NOCS-REAL275 dataset demonstrate the superiority of our method.