3D object recognition in cluttered scenes with local surface features: A survey

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    Abstract

    3D object recognition in cluttered scenes is a rapidly growing research area. Based on the used types of features, 3D object recognition methods can broadly be divided into two categories-global or local feature based methods. Intensive research has been done on local surface feature based methods as they are more robust to occlusion and clutter which are frequently present in a real-world scene. This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods. These methods generally comprise three phases: 3D keypoint detection, local surface feature description, and surface matching. This paper covers an extensive literature survey of each phase of the process. It also enlists a number of popular and contemporary databases together with their relevant attributes.
    Original languageEnglish
    Pages (from-to)2270-2287
    Number of pages18
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume36
    Issue number11
    Early online date11 Apr 2014
    DOIs
    Publication statusPublished - 1 Nov 2014

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    3D Object Recognition
    Object recognition
    Local Features
    Clutter
    Occlusion
    Attribute
    Cover

    Cite this

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    title = "3D object recognition in cluttered scenes with local surface features: A survey",
    abstract = "3D object recognition in cluttered scenes is a rapidly growing research area. Based on the used types of features, 3D object recognition methods can broadly be divided into two categories-global or local feature based methods. Intensive research has been done on local surface feature based methods as they are more robust to occlusion and clutter which are frequently present in a real-world scene. This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods. These methods generally comprise three phases: 3D keypoint detection, local surface feature description, and surface matching. This paper covers an extensive literature survey of each phase of the process. It also enlists a number of popular and contemporary databases together with their relevant attributes.",
    author = "Yulan Guo and Mohammed Bennamoun and Ferdous Sohel and M. Lu and J. Wan",
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    AU - Guo, Yulan

    AU - Bennamoun, Mohammed

    AU - Sohel, Ferdous

    AU - Lu, M.

    AU - Wan, J.

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    AB - 3D object recognition in cluttered scenes is a rapidly growing research area. Based on the used types of features, 3D object recognition methods can broadly be divided into two categories-global or local feature based methods. Intensive research has been done on local surface feature based methods as they are more robust to occlusion and clutter which are frequently present in a real-world scene. This paper presents a comprehensive survey of existing local surface feature based 3D object recognition methods. These methods generally comprise three phases: 3D keypoint detection, local surface feature description, and surface matching. This paper covers an extensive literature survey of each phase of the process. It also enlists a number of popular and contemporary databases together with their relevant attributes.

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