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Depth from water reflection.

Linjie Yang, Jianzhuang Liu, Xiaoou Tang

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    This study introduces a novel framework for reconstructing depth from single water reflection images by adapting mirror scene appearances and employing stereo matching. This method effectively recovers depth maps for diverse scenes, including natural and man-made objects.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Photogrammetry

    Background:

    • Water reflection images exhibit natural bilateral symmetry, offering potential for depth reconstruction.
    • Reconstructing depth from reflections is challenging due to significant appearance differences between real and mirrored scenes.

    Purpose of the Study:

    • To develop a framework for single water reflection image depth reconstruction.
    • To address the challenge of appearance disparity in stereo vision for reflection scenarios.

    Main Methods:

    • An appearance adaptation method is proposed to align the visual characteristics of the mirrored scene with the real scene.
    • A stereo matching algorithm is utilized to compute the disparity map of the actual scene.

    Main Results:

    • The appearance adaptation significantly reduces the discrepancy between real and mirrored scene appearances.
    • The stereo matching algorithm successfully generates accurate disparity maps from adapted reflection images.
    • The framework demonstrates robustness in recovering depth for scenes containing both natural and man-made objects.

    Conclusions:

    • The proposed framework enables effective depth reconstruction from single water reflection images.
    • This approach advances depth estimation techniques by handling appearance variations inherent in reflections.
    • The method broadens the applicability of depth-from-symmetry techniques beyond man-made objects to complex natural scenes.