  In this paper, we propose the USIP detector: an Unsupervised Stable Interest
Point detector that can detect highly repeatable and accurately localized
keypoints from 3D point clouds under arbitrary transformations without the need
for any ground truth training data. Our USIP detector consists of a feature
proposal network that learns stable keypoints from input 3D point clouds and
their respective transformed pairs from randomly generated transformations. We
provide degeneracy analysis of our USIP detector and suggest solutions to
prevent it. We encourage high repeatability and accurate localization of the
keypoints with a probabilistic chamfer loss that minimizes the distances
between the detected keypoints from the training point cloud pairs. Extensive
experimental results of repeatability tests on several simulated and real-world
3D point cloud datasets from Lidar, RGB-D and CAD models show that our USIP
detector significantly outperforms existing hand-crafted and deep
learning-based 3D keypoint detectors. Our code is available at the project
website. https://github.com/lijx10/USIP
