SHORT: A fast 3D feature description based on estimating occupancy in spherical shell regions

Shoichi Takei, Shuichi Akizuki, Manabu Hashimoto

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

    Abstract

    We propose a novel feature description method called SHORT (Shell Histograms and Occupancy from Radial Transform) for fast 3D object recognition. In 3D object recognition for point cloud data, it is very important to detect keypoints and describe features rapidly because of the huge amount of data involved. The state-of-the-art keypoint detection methods calculate statistics including covariance matrices from the point cloud in local regions of the object. Then, the state-of-the-art method, which describe features such as normal vector distributions of the point cloud, use all points in the local regions. However, these methods involve high processing costs because they need to calculate the statistics needed for keypoint detection. They also need to use a lot of points in the regions for feature description. By contrast, the SHORT method consists of a fast keypoint detector that does not calculate statistics and a fast feature descriptor that uses only a small number of points in the restricted local regions. The keypoint detector uses occupancy estimated simply like counting the points in regions of outermost shells in spheres, and the feature descriptor uses estimated those and a small number of points including the spherical shell regions of multiple scales. Experimental results in 3D object recognition show that the processing speed of the proposed method is five times faster than that of a comparative method that had a nearly equal 99.4% recognition success rate.

    Original languageEnglish
    Title of host publication2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015
    PublisherIEEE Computer Society
    ISBN (Electronic)9781509003570
    DOIs
    Publication statusPublished - 2016 Nov 28
    Event2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015 - Auckland, New Zealand
    Duration: 2015 Nov 232015 Nov 24

    Publication series

    NameInternational Conference Image and Vision Computing New Zealand
    Volume2016-November
    ISSN (Print)2151-2191
    ISSN (Electronic)2151-2205

    Other

    Other2015 International Conference on Image and Vision Computing New Zealand, IVCNZ 2015
    CountryNew Zealand
    CityAuckland
    Period15/11/2315/11/24

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Computer Vision and Pattern Recognition
    • Electrical and Electronic Engineering

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