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    Area of Science:

    • Computer Graphics
    • Data Visualization

    Background:

    • Occlusions in visualizing large, complex datasets limit information bandwidth.
    • Conventional single-viewpoint visualizations restrict visibility to direct line-of-sight elements.

    Purpose of the Study:

    • To present a flexible framework for designing multiperspective focus+context visualizations.
    • To alleviate occlusion problems in data visualization through camera model manipulation.

    Main Methods:

    • Developed a framework for multiperspective visualization by manipulating the camera model.
    • Adapted focus region viewpoints to reduce occlusions.
    • Supported three visualization scenarios: independent region viewpoint alteration, connecting conventional images, and visualizing occluded targets.

    Main Results:

    • Demonstrated flexible design of multiperspective visualizations.
    • Achieved interactive rendering rates by leveraging fast camera projection.
    • Successfully applied the framework to terrain, urban, molecular biology, and volume datasets.

    Conclusions:

    • The proposed framework effectively alleviates occlusions in complex datasets.
    • Multiperspective visualization enhances information visibility and bandwidth.
    • The camera model manipulation offers a flexible and efficient approach to occlusion handling.