Self-supervised learning for domain adaptation on point clouds

Idan Achituve, Haggai Maron, Gal Chechik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

87 Scopus citations

Abstract

Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data. It has been applied effectively to domain adaptation (DA) on images and videos. It is still unknown if and how it can be leveraged for domain adaptation in 3D perception problems. Here we describe the first study of SSL for DA on point clouds. We introduce a new family of pretext tasks, Deformation Reconstruction, inspired by the deformations encountered in sim-to-real transformations. In addition, we propose a novel training procedure for labeled point cloud data motivated by the MixUp method called Point cloud Mixup (PCM). Evaluations on domain adaptations datasets for classification and segmentation, demonstrate a large improvement over existing and baseline methods.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages123-133
Number of pages11
ISBN (Electronic)9780738142661
DOIs
StatePublished - Jan 2021
Event2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021 - Virtual, Online, United States
Duration: 5 Jan 20219 Jan 2021

Publication series

NameProceedings - 2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021

Conference

Conference2021 IEEE Winter Conference on Applications of Computer Vision, WACV 2021
Country/TerritoryUnited States
CityVirtual, Online
Period5/01/219/01/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

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