Self-Supervised point set local descriptors for Point Cloud Registration
Zitieren Sie bitte immer diese URN: urn:nbn:de:bvb:20-opus-223000
- 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 trainingDescriptors 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.…
Autor(en): | Yijun Yuan, Dorit BorrmannORCiD, Jiawei Hou, Yuexin Ma, Andreas NüchterORCiD, Sören Schwertfeger |
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URN: | urn:nbn:de:bvb:20-opus-223000 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Sensors |
ISSN: | 1424-8220 |
Erscheinungsjahr: | 2021 |
Band / Jahrgang: | 21 |
Heft / Ausgabe: | 2 |
Aufsatznummer: | 486 |
Originalveröffentlichung / Quelle: | Sensors 2021, 21(2), 486; https://doi.org/10.3390/s21020486 |
DOI: | https://doi.org/10.3390/s21020486 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 005 Computerprogrammierung, Programme, Daten |
Freie Schlagwort(e): | descriptors; point cloud registration; self-supervised learning |
Datum der Freischaltung: | 30.09.2021 |
Datum der Erstveröffentlichung: | 12.01.2021 |
Open-Access-Publikationsfonds / Förderzeitraum 2021 | |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |