Instance-Adaptive and Geometric-Aware Keypoint Learning for
  Category-Level 6D Object Pose Estimation
Xiao Lin Wenfei Yang Yuan Gao Tianzhu Zhang
Category-level 6D object pose estimation aims to estimate the rotation,
translation and size of unseen instances within specific categories. In this
area, dense correspondence-based methods have achieved leading performance.
However, they do not explicitly consider the local and global geometric
information of different instances, resulting in poor generalization ability to
unseen instances with significant shape variations. To deal with this problem,
we propose a novel Instance-Adaptive and Geometric-Aware Keypoint Learning
method for category-level 6D object pose estimation (AG-Pose), which includes
two key designs: (1) The first design is an Instance-Adaptive Keypoint
Detection module, which can adaptively detect a set of sparse keypoints for
various instances to represent their geometric structures. (2) The second
design is a Geometric-Aware Feature Aggregation module, which can efficiently
integrate the local and global geometric information into keypoint features.
These two modules can work together to establish robust keypoint-level
correspondences for unseen instances, thus enhancing the generalization ability
of the model.Experimental results on CAMERA25 and REAL275 datasets show that
the proposed AG-Pose outperforms state-of-the-art methods by a large margin
without category-specific shape priors.