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Simultaneous Localization and Appearance Estimation with a Consumer RGB-D Camera.

Hongzhi Wu, Zhaotian Wang, Kun Zhou

    IEEE Transactions on Visualization and Computer Graphics
    |November 13, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method to estimate surface reflectance from RGB-D camera images. It improves material appearance acquisition by jointly optimizing camera poses, lighting, and surface properties.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Material Science

    Background:

    • Acquiring accurate material appearance from consumer RGB-D cameras is challenging due to pose/geometry inaccuracies and coupled lighting.
    • Existing methods struggle with unknown lighting conditions and precise material property estimation.

    Purpose of the Study:

    • To develop a novel technique for estimating spatially varying isotropic surface reflectance.
    • To enable casual users to acquire general material appearance using hand-held RGB-D cameras.
    • To address challenges of inaccurate camera poses, geometry, and unknown lighting.

    Main Methods:

    • A joint optimization framework is proposed, alternating between solving for camera poses, materials, lighting, and normals.
    • Camera poses are refined by exploiting material variations, treating the object as a self-calibrating model.
    • Unknown lighting is recovered using color images and material estimates in a global optimization solved in the wavelet domain.

    Main Results:

    • The technique successfully estimates surface reflectance solely from RGB-D data under unknown illumination.
    • Joint optimization significantly improves the accuracy of reconstructed camera poses and material properties.
    • Demonstrated substantially improved quality of estimated appearance on various daily objects.

    Conclusions:

    • The presented method offers a robust solution for material appearance acquisition using consumer RGB-D cameras.
    • It effectively disentangles material properties from unknown lighting conditions.
    • Enables more accessible and accurate 3D material capture for various applications.