Geometrical and statistical incremental semantic modeling on mobile devices

Rafael Roberto, João Paulo Lima, Hideaki Uchiyama, Veronica Teichrieb, Rin ichiro Taniguchi

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)


Improvements on mobile devices allowed tracking applications to be executed on such platforms. However, there still remain several challenges in the field of mobile tracking, such as the extraction of high-level semantic information from point clouds. This task is more challenging when using monocular visual SLAM systems that output noisy sparse data. In this paper, we propose a primitive modeling method using both geometric and statistical analyses for sparse point clouds that can be executed on mobile devices. The main idea is to use the incremental mapping process of SLAM systems for analyzing the geometric relationship between the point cloud and the estimated shapes over time and selecting only reliably-modeled shapes. Besides that, a statistical evaluation that assesses if the modeling is random is incorporated to filter wrongly-detected primitives in unstable estimations. Our evaluation indicates that the proposed method was able to improve both precision and consistency of correct detection when compared with existing techniques. The mobile version execution is 8.5 to 9.9 times slower in comparison with the desktop implementation. However, it uses up to 30.5% of CPU load, which allows it to run on a separate thread, in parallel with the visual SLAM technique. Additional evaluations show that CPU load, energy consumption and RAM memory usage were not a concern when running our method on mobile devices.

Original languageEnglish
Pages (from-to)199-211
Number of pages13
JournalComputers and Graphics (Pergamon)
Publication statusPublished - 1 Nov 2019

Bibliographical note

Funding Information:
The authors would like to thank Clemens Arth and Dieter Schmalstieg for the valuable discussions. This work was partially funded by JSPS KAKENHI (grant number JP17H01768).

Publisher Copyright:
© 2019 Elsevier Ltd


  • Android
  • Mobile devices
  • Semantic tracking
  • Tracking
  • Visual SLAM

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • General Engineering
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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