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Outdoor Inverse Rendering From a Single Image Using Multiview Self-Supervision.

Ye Yu, William A P Smith

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 9, 2021
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    Summary
    This summary is machine-generated.

    This study introduces a novel neural network for inverse rendering, recovering scene shape, reflectance, and lighting from a single image. It leverages self-supervision and multiview stereo for robust and accurate results without ground truth data.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Inverse rendering aims to recover scene properties like shape, reflectance, and lighting from images.
    • Traditional methods often require controlled environments or multiple images, limiting their applicability.
    • Uncontrolled, single-image inverse rendering remains a challenging ill-posed problem.

    Purpose of the Study:

    • To develop a fully convolutional neural network for scene-level inverse rendering from a single, uncontrolled RGB image.
    • To enable the recovery of albedo, shadow, and normal maps, and infer lighting coefficients.
    • To introduce novel self-supervision and multiview stereo (MVS) based supervision techniques for training.

    Main Methods:

    • A fully convolutional neural network regresses albedo, shadow, and normal maps from an input RGB image.
    • Least squares optimal spherical harmonic lighting coefficients are inferred from the estimated maps.
    • The network is trained using self-supervision via a differentiable renderer and additional supervision from offline MVS.
    • Siamese training ensures consistent estimation of photometric invariants using MVS pose and depth maps.
    • A statistical natural illumination prior is learned.

    Main Results:

    • The proposed method successfully performs scene-level inverse rendering, recovering shape, reflectance, and lighting.
    • MVS-derived supervision provides direct coarse supervision for normal map estimation.
    • The approach demonstrates effectiveness on inverse rendering, normal map estimation, and intrinsic image decomposition benchmarks.
    • This work represents the first attempt to utilize MVS supervision for learning inverse rendering.

    Conclusions:

    • The integration of self-supervision and MVS-based supervision enables robust inverse rendering from single, uncontrolled images.
    • The developed network can accurately estimate scene properties and lighting without ground truth data.
    • This research advances the capabilities of single-image inverse rendering and intrinsic image decomposition.