  Understanding point clouds is of great importance. Many previous methods
focus on detecting salient keypoints to identity structures of point clouds.
However, existing methods neglect the semantics of points selected, leading to
poor performance on downstream tasks. In this paper, we propose Keypoint
Autoencoder, an unsupervised learning method for detecting keypoints. We
encourage selecting sparse semantic keypoints by enforcing the reconstruction
from keypoints to the original point cloud. To make sparse keypoint selection
differentiable, Soft Keypoint Proposal is adopted by calculating weighted
averages among input points. A downstream task of classifying shape with sparse
keypoints is conducted to demonstrate the distinctiveness of our selected
keypoints. Semantic Accuracy and Semantic Richness are proposed and our method
gives competitive or even better performance than state of the arts on these
two metrics.
