Fine segmentation and difference-aware shape adjustment for category-level 6DoF object pose estimation

Six-degree-of-freedom (6DoF) object pose estimation is a critical task for robot manipulation, autonomous vehicles, and augmented reality. Category-level 6DoF object pose estimation is trending because it can generalize to same-category unknown objects. However, existing mean shape based methods do not consider that predicting adjustment must model shape differences, which makes these methods still suffer from shape variations among same-category objects, limiting their accuracy. Also, existing methods overlook the importance of object segmentation to 6DoF pose estimation and use an RGB-based object segmentation method with low accuracy. To address these problems, we propose difference-aware shape adjustment and RGB-D feature fusion-based object segmentation for category-level 6DoF object pose estimation. The proposed method encodes shape differences, improving mean shape adjustment and alleviating same-category shape variations. Specifically, a difference-aware shape adjustment network (DASAN) is proposed to model shape differences between the object instance and mean shape by feature subtraction with an attention mechanism. We also propose an RGB-D feature fusion-based object segmentation method that uses a coarse-to-fine framework: a 2D detector and a novel RGB-D feature fusion-based binary classification network for coarse and fine segmentation. Experiments on two well-known datasets demonstrate the proposed method’s state-of-the-art (SOTA) pose estimation accuracy. In addition, we construct comparative experiments on the latest dataset (Wild6D) and a self-collected dataset (OBJECTS) and achieve high accuracies, demonstrating the strong generalizability of the proposed method. Also, we apply the proposed method to unknown object grasping, thus demonstrating the practicability of the proposed method.