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

Yulan Guo, Mohammed Bennamoun, Ferdous Sohel, M. Lu, J. Wan

    Research output: Contribution to journalArticlepeer-review

    514 Citations (Scopus)
    12 Downloads (Pure)


    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
    Issue number11
    Early online date11 Apr 2014
    Publication statusPublished - 1 Nov 2014


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