Localizing predefined 3D keypoints in a 2D image is an effective way to establish 3D-2D correspondences for 6DoF object pose estimation. However, unreliable localization results of invisible keypoints degrade the quality of correspondences. In this paper, we address this issue by localizing the important keypoints in terms of visibility. Since keypoint visibility information is currently missing in dataset collection process, we propose an efficient way to generate binary visibility labels from available object-level annotations, for keypoints of both asymmetric objects and symmetric objects. We further derive real-valued visibility-aware importance from binary labels based on PageRank algorithm. Taking advantage of the flexibility of our visibility-aware importance, we construct VAPO (Visibility-Aware POse estimator) by integrating the visibility-aware importance with a state-of-the-art pose estimation algorithm, along with additional positional encoding. Extensive experiments are conducted on popular pose estimation benchmarks including Linemod, Linemod-Occlusion, and YCB-V. The results show that, VAPO improves both the keypoint correspondences and final estimated poses, and clearly achieves state-of-the-art performances.

翻译：在二维图像中定位预定义的三维关键点是建立6DoF物体姿态估计中3D-2D对应关系的有效方法。然而，不可见关键点的不可靠定位结果会降低对应关系的质量。本文通过根据可见性定位重要关键点来解决这一问题。由于当前数据集采集过程中缺少关键点可见性信息，我们提出了一种高效方法，从可用的物体级标注中为不对称物体和对称物体的关键点生成二元可见性标签。进一步基于PageRank算法，从二元标签中推导出实数值的可见性感知重要性。利用可见性感知重要性的灵活性，我们通过将可见性感知重要性与最新姿态估计算法及附加的位置编码相结合，构建了VAPO（可见性感知姿态估计器）。在包括Linemod、Linemod-Occlusion和YCB-V在内的主流姿态估计基准上进行了大量实验。结果表明，VAPO不仅改进了关键点对应关系，还提升了最终估计姿态的质量，并明显达到了最先进的性能水平。