Incremental Structural Modeling Based on Geometric and Statistical Analyses

Rafael Roberto, João Paulo Lima, Hideaki Uchiyama, Clemens Arth, Veronica Teichrieb, Rin Ichiro Taniguchi, DIeter Schmalstieg

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)

Abstract

Finding high-level semantic information from a point cloud is a challenging task, and it can be used in various applications. For instance, it is useful to compactly represent the scene structure and efficiently understand the scene context. This task is even more challenging when using a hand-held monocular visual SLAM system that outputs a noisy sparse point cloud. In order to tackle this issue, we propose an incremental primitive modeling method using both geometric and statistical analyses for such point cloud. The main idea is to select only reliably-modeled shapes by analyzing the geometric relationship between the point cloud and the estimated shapes. Besides that, a statistical evaluation is incorporated to filter wrongly-detected primitives in a noisy point cloud. As a result of this processing, our approach largely improved precision when compared with state of the art methods. We also show the impact of segmenting and representing a scene using primitives instead of a point cloud.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PublisherIEEE
Pages955-963
Number of pages9
ISBN (Electronic)9781538648865
DOIs
Publication statusPublished - 3 May 2018
Event18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018 - Lake Tahoe, USA United States
Duration: 12 Mar 201815 Mar 2018

Publication series

NameProceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Volume2018-January

Conference

Conference18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Country/TerritoryUSA United States
CityLake Tahoe
Period12/03/1815/03/18

Bibliographical note

Funding Information:
The authors would like to thank CNPq (process 140898/2014-0and456800/2014-0), CAPES(process 88881.134246/2016-01)andtheAustrianFFGunderthe Matahariprojectnr. 859208forpartiallyfundingthis research.

Funding Information:
The authors would like to thank CNPq (process 140898/2014-0 and 456800/2014-0), CAPES (process 88881.134246/2016-01) and the Austrian FFG under the Matahari project nr. 859208 for partially funding this research.

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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