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Determining 3D Flow Fields via Multi-camera Light Field Imaging
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View-Guided Cost Volume for Light Field Arbitrary-View Disparity Estimation.

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    IEEE Transactions on Visualization and Computer Graphics
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    This study introduces a novel view-guided cost volume (VGCV) for accurate light field (LF) arbitrary-view disparity estimation. The proposed method enhances disparity map quality for diverse applications like light field editing.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Light Field Technology

    Background:

    • Per-view disparity estimation is crucial for light field (LF) applications.
    • Existing methods primarily focus on center-view disparity, limiting broader applications.
    • There's a need for methods capable of arbitrary-view disparity estimation in LFs.

    Purpose of the Study:

    • To develop a novel approach for high-quality disparity map generation for arbitrary views in light fields.
    • To overcome the limitations of center-view focused disparity estimation methods.
    • To improve the adaptability and performance of disparity estimation across various LF views.

    Main Methods:

    • Proposed a view-guided cost volume (VGCV) that incorporates spatial and angular information of the target view.
    • Introduced an image-guided construction module with cross-view attention to preserve structural information.
    • Developed a coordinate-guided aggregation module to address light field view deviation issues.
    • Implemented the Light Field Arbitrary-View Disparity Estimation Network (LFAVNet).

    Main Results:

    • VGCV effectively adapts to arbitrary-view disparity estimation tasks.
    • The image-guided construction and coordinate-guided aggregation modules enhance cost volume quality.
    • LFAVNet achieves superior performance in generating high-quality disparity maps for arbitrary views on both synthetic and real light fields.
    • Extended method shows advanced performance in center-view estimation and light field editing.

    Conclusions:

    • The proposed VGCV and LFAVNet provide a robust solution for light field arbitrary-view disparity estimation.
    • The method significantly improves disparity map quality and demonstrates versatility across related tasks.
    • This work advances the capabilities of light field analysis and manipulation.