MMFEIR: Multi-attention Mutual Feature Enhance and Instance Reconstruction for category-level 6D object pose estimation

Category-level 6D object pose estimation is a fundamental problem in fields such as robotic manipulation and augmented reality. The goal of this task is to predict the rotation, translation, and size of the object. Current research typically extracts the deformation field from observed point cloud of the object for estimating 6D pose. However, they did not fully consider the interaction between the observed point cloud, prior shape, and image of the object, resulting in the loss of geometric and texture features of the object, thereby affecting the accuracy of pose estimation for objects with large intra class configuration differences. In this paper, we propose a Multi-attention Mutual Feature Enhance Module (MMFEM) to enhance the inherent linkages among different perception data of objects. MMFEM enhances the interaction between images, observed point cloud, and prior shape through multiple attention modules. This enables the network to gain a deeper understanding of the differences between distinct instances. In addition, to improve the feature expression of geometric details for objects, we propose the Instance Reconstruction Deformation Module (IRDM). IRDM reconstructed the three-dimensional instance point cloud for each object, enhancing the model’s ability to identify differences in geometric configurations of objects. Extensive experiments on the CAMERA25 and REAL275 datasets show that the proposed methods have achieved 79.0% and 91.2% on the 3D75 metric, 52.6% and 75.9% on the 5°2 cm metric, respectively, outperforming current mainstream methods.