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Robust, Efficient Depth Reconstruction With Hierarchical Confidence-Based Matching.

Li Sun, Ke Chen, Mingli Song

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 31, 2017
    PubMed
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    This study introduces a new hierarchical framework for depth reconstruction using mobile devices. It achieves robust and efficient results in uncontrolled scenes by combining local and global optimization techniques.

    Area of Science:

    • Computer Vision
    • Mobile Imaging

    Background:

    • Mobile devices are increasingly used for photography and videography, driving demand for advanced applications like depth reconstruction.
    • Existing depth reconstruction methods struggle with challenges like occlusions, non-diffuse surfaces, and uncontrolled mobile imaging conditions.

    Purpose of the Study:

    • To develop a novel hierarchical framework for robust and efficient depth reconstruction in uncontrolled scenes using mobile devices.
    • To improve accuracy and efficiency by combining local cost aggregation with global cost optimization.

    Main Methods:

    • A hierarchical framework utilizing multi-view confidence-based matching.
    • Coarse-to-fine depth map generation using an image pyramid.
    • Confidence maps for robust multi-view cue fusion and stereo matching constraints.

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    Main Results:

    • The proposed framework demonstrates robust and efficient depth reconstruction capabilities.
    • Successful evaluation on challenging indoor and outdoor scenes.

    Conclusions:

    • The novel framework effectively addresses limitations in mobile depth reconstruction.
    • The combination of hierarchical processing and confidence-based matching enhances accuracy and efficiency in uncontrolled environments.