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Large-Scale Point-Cloud Visualization through Localized Textured Surface Reconstruction.

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    This study presents a new method for visualizing large point clouds with photographs, significantly speeding up texture generation. The approach efficiently handles large datasets by processing overlapping surface patches locally.

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    Area of Science:

    • Computer Graphics
    • Geometric Modeling
    • Data Visualization

    Background:

    • Large-scale point clouds often have associated high-resolution photographs.
    • Lighting variations and registration inaccuracies in photographs complicate data representation.
    • High-quality visualization of combined point cloud and photographic data is challenging and time-consuming.

    Purpose of the Study:

    • To introduce a novel scene representation for visualizing large-scale point clouds with photographs.
    • To develop a faster and more flexible method for seamless texture generation and data visualization.

    Main Methods:

    • A two-phase approach involving local surface patch generation and render-time stitching.
    • Preprocessing phase: Generates overlapping surface patches and performs local, seamless texture generation.
    • Second phase: Stitches patches at render-time for high-quality visualization.

    Main Results:

    • The proposed algorithm is over an order of magnitude faster than mesh-based texturing techniques.
    • Achieves high-quality visualization of point clouds and associated photographs.
    • The localized approach allows for efficient handling of growing datasets.

    Conclusions:

    • The novel two-phase method significantly improves the speed and flexibility of visualizing large-scale point clouds with photographic data.
    • Localizing the texturing problem leads to substantial performance gains.
    • The technique is well-suited for applications with massive and expanding datasets.