  In this work, we propose to learn local descriptors for point clouds in a
self-supervised manner. In each iteration of the training, the input of the
network is merely one unlabeled point cloud. On top of our previous work, that
directly solves the transformation between two point sets in one step without
correspondences, the proposed method is able to train from one point cloud, by
supervising its self-rotation, that we randomly generate. The whole training
requires no manual annotation. In several experiments we evaluate the
performance of our method on various datasets and compare to other state of the
art algorithms. The results show, that our self-supervised learned descriptor
achieves equivalent or even better performance than the supervised learned
model, while being easier to train and not requiring labeled data.
