DPN-LRF: A local reference frame for robustly handling density differences and partial occlusions

Shuichi Akizuki, Manabu Hashimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

For the purpose of 3D keypoint matching, a Local Reference Frame (LRF), a local coordinate system of the keypoint, is one important information source for achieving repeatable feature descriptions and accurate pose estimations. We propose a robust LRF for two main point cloud disturbances: density differences and partial occlusions. To generate LRFs that are robust to such disturbances, we employ two strategies: normalizing the effects of point cloud density by approximating the surface area in the local region and using the dominant orientation of a normal vector around the keypoint. Experiments confirm that the proposed method has higher repeatability than state-of-the-art methods with respect to density differences and partial occlusions. It was also confirmed that the method enhances the reliability of keypoint matching.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings
PublisherSpringer Verlag
Pages878-887
Number of pages10
Volume9474
ISBN (Print)9783319278568
DOIs
Publication statusPublished - 2015
Externally publishedYes
Event11th International Symposium on Advances in Visual Computing, ISVC 2015 - Las Vegas, United States
Duration: 2015 Dec 142015 Dec 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9474
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Symposium on Advances in Visual Computing, ISVC 2015
CountryUnited States
CityLas Vegas
Period15/12/1415/12/16

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Akizuki, S., & Hashimoto, M. (2015). DPN-LRF: A local reference frame for robustly handling density differences and partial occlusions. In Advances in Visual Computing - 11th International Symposium, ISVC 2015, Proceedings (Vol. 9474, pp. 878-887). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9474). Springer Verlag. https://doi.org/10.1007/978-3-319-27857-5_78