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3D Object Recognition in Cluttered Scenes with Local Surface Features: A Survey.

Yulan Guo, Mohammed Bennamoun, Ferdous Sohel

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This survey reviews local surface feature methods for 3D object recognition in cluttered scenes. It details keypoint detection, feature description, and matching phases, crucial for robust real-world applications.

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    Area of Science:

    • Computer Vision
    • Robotics
    • 3D Data Processing

    Background:

    • 3D object recognition is vital for real-world applications.
    • Cluttered scenes pose significant challenges due to occlusion and noise.
    • Existing methods often rely on global or local features.

    Purpose of the Study:

    • To provide a comprehensive survey of local surface feature-based 3D object recognition methods.
    • To analyze the distinct phases involved in these recognition processes.
    • To highlight contemporary databases and their attributes for research.

    Main Methods:

    • Literature review of 3D object recognition techniques.
    • Categorization based on feature types (global vs. local).
    • Detailed examination of methods focusing on local surface features.

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    Main Results:

    • Local surface feature methods demonstrate robustness against occlusion and clutter.
    • Recognition processes typically involve 3D keypoint detection, local feature description, and surface matching.
    • A curated list of relevant 3D object recognition databases is presented.

    Conclusions:

    • Local surface feature-based methods are highly effective for 3D object recognition in challenging environments.
    • Understanding the key phases is essential for developing advanced recognition systems.
    • The survey provides a valuable resource for researchers in the field.