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Image interpolation used in three-dimensional range data compression.

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    This study introduces a new framework to compress large three-dimensional (3D) range data by converting it into images and using interpolation. This method effectively reduces data size for storage and transmission while maintaining accuracy.

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    Area of Science:

    • Computer Vision
    • Data Compression
    • 3D Imaging

    Background:

    • Three-dimensional (3D) scanning generates large datasets, posing storage and transmission challenges.
    • Efficient compression techniques are crucial for managing the increasing volume of 3D range data.

    Purpose of the Study:

    • To present a novel framework for further compressing 3D range data.
    • To address the challenge of storing and transmitting large 3D range datasets.

    Main Methods:

    • A virtual fringe-projection system is used to convert 3D range data into images.
    • Image interpolation is applied to reduce image resolution, thereby compressing the 3D data.
    • The compressed data is decompressed by scaling up the low-resolution image and decoding.

    Main Results:

    • The proposed method successfully reduces the data size of 3D range data.
    • Experimental results demonstrate a low error rate in data recovery after compression and decompression.

    Conclusions:

    • The image interpolation-based framework offers an effective solution for 3D range data compression.
    • This approach facilitates more efficient storage and transmission of 3D scanning data.