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Surface Estimation of Translucent Materials: An Application to Fabric Digitization.

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    Summary

    We developed a new method to estimate the surface of translucent objects using inverse rendering and multiple light sources. This technique accurately reconstructs surface meshes, even for challenging materials like fabrics.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Materials Science

    Background:

    • Estimating surfaces of translucent objects is difficult due to light scattering.
    • Existing methods struggle with complex internal light interactions.

    Purpose of the Study:

    • To develop a novel method for reconstructing surface meshes of translucent materials.
    • To derive volumetric properties and surface geometry from photometric data.

    Main Methods:

    • Utilized inverse rendering on single-viewpoint photographs under multiple illuminations.
    • Computed depth maps and derived volumetric representations (density, albedo, phase function).
    • Reconstructed surface meshes from the volumetric data.

    Main Results:

    • Successfully validated the method with synthetic and 3D-printed models.
    • Demonstrated effective application to fabric digitization, a challenging translucent material.
    • Achieved enhanced surface reconstruction for texture stack acquisition.

    Conclusions:

    • The proposed inverse rendering approach enables accurate surface estimation for translucent objects.
    • This method significantly improves the digitization of complex materials like textiles.
    • Advances the field of texture acquisition and surface reconstruction.