  Keypoint detection is an essential component for the object registration and
alignment. In this work, we reckon keypoint detection as information
compression, and force the model to distill out irrelevant points of an object.
Based on this, we propose UKPGAN, a general self-supervised 3D keypoint
detector where keypoints are detected so that they could reconstruct the
original object shape. Two modules: GAN-based keypoint sparsity control and
salient information distillation modules are proposed to locate those important
keypoints. Extensive experiments show that our keypoints align well with human
annotated keypoint labels, and can be applied to SMPL human bodies under
various non-rigid deformations. Furthermore, our keypoint detector trained on
clean object collections generalizes well to real-world scenarios, thus further
improves geometric registration when combined with off-the-shelf point
descriptors. Repeatability experiments show that our model is stable under both
rigid and non-rigid transformations, with local reference frame estimation. Our
code is available on https://github.com/qq456cvb/UKPGAN.
