TY  - JOUR
A1  - Yuan, Yijun
A1  - Borrmann, Dorit
A1  - Hou, Jiawei
A1  - Ma, Yuexin
A1  - Nüchter, Andreas
A1  - Schwertfeger, Sören
T1  - Self-Supervised point set local descriptors for Point Cloud Registration
T2  - Sensors
N2  - Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration 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. Thus, the whole training requires no manual annotation and manual selection of patches. In addition, we propose to involve keypoint sampling into the pipeline, which further improves the performance of our model. Our experiments demonstrate the capability of our self-supervised local descriptor to achieve even better performance than the supervised model, while being easier to train and requiring no data labeling.
KW  - point cloud registration
KW  - descriptors
KW  - self-supervised learning
Y1  - 2021
UR  - https://opus.bibliothek.uni-wuerzburg.de/frontdoor/index/index/docId/22300
UR  - https://nbn-resolving.org/urn:nbn:de:bvb:20-opus-223000
SN  - 1424-8220
VL  - 21
IS  - 2
ER  -