RGB-D SLAM based incremental cuboid modeling

Masashi Mishima, Hideaki Uchiyama, Diego Thomas, Rin ichiro Taniguchi, Rafael Roberto, João Paulo Lima, Veronica Teichrieb

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

1 Citation (Scopus)


This paper present a framework for incremental 3D cuboid modeling combined with RGB-D SLAM. While performing RGB-D SLAM, planes are incrementally reconstructed from point clouds. Then, cuboids are detected in the planes by analyzing the positional relationships between the planes; orthogonality, convexity, and proximity. Finally, the position, pose and size of a cuboid are determined by computing the intersection of three perpendicular planes. In addition, the cuboid shapes are incrementally updated to suppress false detections with sequential measurements. As an application of our framework, an augmented reality based interactive cuboid modeling system is introduced. In the evaluation at a cluttered environment, the precision and recall of the cuboid detection are improved with our framework owing to stable plane detection, compared with a batch based method.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 Workshops, Proceedings
EditorsLaura Leal-Taixé, Stefan Roth
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783030110086
Publication statusPublished - 2019
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: Sep 8 2018Sep 14 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11129 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th European Conference on Computer Vision, ECCV 2018

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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