A new view planning method for automatic modeling of three dimensional objects

Xiaolong Zhou, Bingwei He*, Y. F. Li

*Corresponding author for this work

    Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

    3 Citations (Scopus)

    Abstract

    Sensor planning is a critical issue since a typical 3-D sensor can only sample a portion of an object at a single viewpoint. The primary focus of the research described in this paper is to propose a new method of creating a complete model of free-form surface object from multiple range images acquired by a scan sensor at different space poses. Candidates for the best-next-view position are determined by detecting and measuring occlusions to the camera's view in an image. Ultimately, the candidate which obtains maximum visible space volume is selected as the Next-best-view position. The experimental results show that the method is effective in practical implementation. © 2008 Springer Berlin Heidelberg.
    Original languageEnglish
    Title of host publicationIntelligent Robotics and Applications
    Subtitle of host publicationFirst International Conference, ICIRA 2008, Proceedings
    PublisherSpringer Verlag
    Pages161-170
    Volume5314 LNAI
    ISBN (Print)3540885129, 9783540885122
    DOIs
    Publication statusPublished - 2008
    Event1st International Conference on Intelligent Robotics and Applications, ICIRA 2008 - Wuhan, China
    Duration: 15 Oct 200817 Oct 2008

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume5314 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference1st International Conference on Intelligent Robotics and Applications, ICIRA 2008
    Country/TerritoryChina
    CityWuhan
    Period15/10/0817/10/08

    Research Keywords

    • Next best view
    • Sensor planning
    • Three-dimension (3-D) reconstruction
    • View planning

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