DeepBBS: Deep Best Buddies for Point Cloud Registration release_jdvwtve3lvhphmyiji3z5sd5bq

by Itan Hezroni, Amnon Drory, Raja Giryes, Shai Avidan

Released as a article .

2021  

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.
In text/plain format

Archived Files and Locations

application/pdf  5.6 MB
file_hzfojou5bbhmjl6hd7xqlh27q4
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2021-10-06
Version   v1
Language   en ?
arXiv  2110.03016v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 0e116e70-fcf5-48ba-98c0-b3b46ed95feb
API URL: JSON