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.

翻译：类别级6D物体姿态估计旨在估计特定类别中未见实例的旋转、平移和尺寸。在此领域，基于密集对应关系的方法已取得领先性能。然而，这些方法未显式考虑不同实例的局部与全局几何信息，导致对形状变化显著的未见实例泛化能力不足。为解决此问题，我们提出了一种新颖的类别级6D物体姿态估计方法——实例自适应与几何感知关键点学习（AG-Pose），其中包含两个关键设计：（1）第一个设计是实例自适应关键点检测模块，该模块能自适应地为不同实例检测一组稀疏关键点，以表征其几何结构。（2）第二个设计是几何感知特征聚合模块，该模块能将局部与全局几何信息高效整合到关键点特征中。这两个模块协同工作，可为未见实例建立鲁棒的关键点级对应关系，从而增强模型的泛化能力。在CAMERA25和REAL275数据集上的实验结果表明，所提出的AG-Pose方法在无需类别特定形状先验的情况下，大幅超越了现有最先进方法。