Visibility-Aware Keypoint Localization for 6DoF Object Pose Estimation
Ruyi Lian Haibin Ling
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.