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Layered Scene Models from Single Hazy Images.

Lingyun Zhao, Miles Hansard, Andrea Cavallaro

    IEEE Transactions on Visualization and Computer Graphics
    |May 30, 2018
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    Summary

    This study introduces a novel layered scene model created from a single hazy image. This method reconstructs 3D scenes efficiently, offering visually convincing results without multiple views.

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Image Processing

    Background:

    • Reconstructing 3D scenes from images is crucial for various applications.
    • Existing methods often require multiple views or complex image correspondences.
    • Hazy images present unique challenges due to atmospheric effects.

    Purpose of the Study:

    • To develop a method for constructing layered scene models from single, hazy images.
    • To overcome limitations of traditional multi-view stereo or structure-from-motion techniques.
    • To create visually convincing 3D scene representations with depth variation.

    Main Methods:

    • Estimating depth maps and radiance images using standard dehazing techniques.
    • Segmenting the radiance image into clusters to identify scene planes.

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  • Developing a layered scene model based on estimated scene planes and depth information.
  • Addressing gap filling and depth blending within the layered structure.

    • Main Results:

    • Successfully constructed layered scene models from single hazy images.
    • Demonstrated the ability to estimate depth maps and scene planes effectively.
    • Achieved visually convincing 3D scene reconstructions resembling 'pop-up' models.
    • Showcased systematic solutions for gap filling and depth blending.

    Conclusions:

    • A single hazy image with depth variation is sufficient for layered scene model construction.
    • The proposed method offers a viable alternative to multi-view approaches for 3D scene reconstruction.
    • The resulting layered depth models are visually compelling and demonstrate practical applications.