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SVBRDF-Invariant Shape and Reflectance Estimation from a Light-Field Camera.

Ting-Chun Wang, Manmohan Chandraker, Alexei A Efros

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 22, 2017
    PubMed
    Summary

    This study introduces a new theory for 3D shape recovery of glossy objects using light-field cameras. It overcomes limitations of standard methods by analyzing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) to enable shape and reflectance estimation.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Optics

    Background:

    • Light-field cameras offer one-shot 3D shape capture.
    • Recovering shapes of glossy objects is difficult due to challenges with standard Lambertian cues like photo-consistency.
    • Existing methods struggle with complex reflectance properties of materials like metals and plastics.

    Purpose of the Study:

    • To derive a spatially-varying (SV)BRDF-invariant theory for 3D shape and reflectance recovery from light-field cameras.
    • To address the challenge of capturing glossy object shapes.
    • To enable simultaneous recovery of shape and reflectance from a single light-field image.

    Main Methods:

    • Developed a novel analysis of diffuse plus single-lobe SVBRDFs in a light-field context.
    • Derived an equation relating depths and normals, overcoming direct shape recovery limitations.
    • Employed a polynomial (quadratic) shape prior to resolve depth ambiguity.
    • Validated the theory using synthetic data from the MERL BRDF dataset and real-world examples.

    Main Results:

    • Successfully derived a theory for SVBRDF-invariant shape and reflectance recovery.
    • Demonstrated that an equation relating depths and normals can be established even when direct shape recovery is not possible.
    • Showcased simultaneous recovery of shape and spatially-varying bidirectional reflectance distribution functions (SVBRDFs) from single light-field images.
    • Achieved accurate results on both synthetic and real-world glossy objects.

    Conclusions:

    • The proposed SVBRDF-invariant theory provides a robust method for 3D shape and reflectance recovery of glossy objects using light-field cameras.
    • The use of a polynomial shape prior effectively resolves ambiguities in shape estimation.
    • This work advances the capabilities of passive 3D shape capture for challenging materials.