DeepBBS: Deep Best Buddies for Point Cloud Registration

Itan Hezroni, Amnon Drory, Raja Giryes, Shai Avidan

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

Abstract

Recently,several deep learning approaches have been proposed for point cloud registration. These methods train a network to generate a representation that helps finding matching points in two 3D point clouds. Finding good matches allows them to calculate the transformation between the point clouds accurately. Two challenges of these techniques are dealing with occlusions and generalizing to objects of classes unseen during training. This work proposes DeepBBS,a novel method for learning a representation that takes into account the best buddy distance between points during training. Best Buddies (i.e.,mutual nearest neighbors) are pairs of points nearest to each other. The Best Buddies criterion is a strong indication for correct matches that,in turn,leads to accurate registration. Our experiments show improved performance compared to previous methods. In particular,our learned representation leads to an accurate registration for partial shapes and in unseen categories.

Original languageEnglish
Title of host publicationProceedings - 2021 International Conference on 3D Vision, 3DV 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages342-351
Number of pages10
ISBN (Electronic)9781665426886
DOIs
StatePublished - 2021
Event9th International Conference on 3D Vision, 3DV 2021 - Virtual, Online, United Kingdom
Duration: 1 Dec 20213 Dec 2021

Publication series

NameProceedings - 2021 International Conference on 3D Vision, 3DV 2021

Conference

Conference9th International Conference on 3D Vision, 3DV 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period1/12/213/12/21

Keywords

  • 3d registration
  • best buddies
  • best buddies similarity
  • deep learning
  • deepbbs
  • point cloud registration

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