  In this paper, we tackle the challenging problem of 3D keypoint estimation of
general objects using a novel implicit representation. Previous works have
demonstrated promising results for keypoint prediction through direct
coordinate regression or heatmap-based inference. However, these methods are
commonly studied for specific subjects, such as human bodies and faces, which
possess fixed keypoint structures. They also suffer in several practical
scenarios where explicit or complete geometry is not given, including images
and partial point clouds. Inspired by the recent success of advanced implicit
representation in reconstruction tasks, we explore the idea of using an
implicit field to represent keypoints. Specifically, our key idea is employing
spheres to represent 3D keypoints, thereby enabling the learnability of the
corresponding signed distance field. Explicit keypoints can be extracted
subsequently by our algorithm based on the Hough transform. Quantitative and
qualitative evaluations also show the superiority of our representation in
terms of prediction accuracy.
