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Computer-Generated Animal Model Stimuli
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Live User-Guided Intrinsic Video for Static Scenes.

Abhimitra Meka, Gereon Fox, Michael Zollhofer

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    Summary
    This summary is machine-generated.

    This study introduces a real-time method for user-guided intrinsic decomposition using RGB-D sensors. It enables intuitive scene editing and augmented reality applications by improving reflectance and shading estimation.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Human-Computer Interaction

    Background:

    • Intrinsic decomposition separates scene images into reflectance and shading layers.
    • Existing methods struggle with ill-posed problems and lack real-time user interaction.
    • RGB-D sensors provide depth information crucial for 3D scene understanding.

    Purpose of the Study:

    • To develop a real-time, user-guided intrinsic decomposition method for static scenes captured by RGB-D sensors.
    • To leverage 3D scene geometry for robust constraint fusion and propagation.
    • To enhance intrinsic decomposition quality and enable interactive augmented reality applications.

    Main Methods:

    • Dense volumetric reconstruction to create a 3D scene representation.
    • User constraints (shading/reflectance strokes) applied directly to 3D geometry.
    • 3D constraint fusion and re-projection for novel view propagation.
    • Constraining the ill-posed decomposition problem using fused 3D information.

    Main Results:

    • Achieved real-time user-guided intrinsic decomposition.
    • Demonstrated improved decomposition quality compared to existing techniques.
    • Enabled robust propagation of user constraints to novel views.
    • Showcased live augmented reality applications: object recoloring, scene relighting, material appearance editing.

    Conclusions:

    • The proposed method offers an effective real-time solution for user-guided intrinsic decomposition.
    • Integrating 3D geometry and user constraints significantly improves decomposition robustness and quality.
    • The approach facilitates novel interactive augmented reality experiences by enabling intuitive scene manipulation.