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Related Experiment Video

Updated: Jan 5, 2026

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Joint Stereo Video Deblurring, Scene Flow Estimation and Moving Object Segmentation.

Liyuan Pan, Yuchao Dai, Miaomiao Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |October 16, 2019
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    Summary
    This summary is machine-generated.

    This study introduces a unified framework to jointly deblur stereo videos, estimate 3D scene flow, and segment moving objects. The novel approach significantly improves performance by exploiting connections between these tasks.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • 3D Reconstruction

    Background:

    • Dynamic stereo videos suffer from motion blur and depth variations.
    • Previous methods tackled deblurring, scene flow, and segmentation separately, missing task interdependencies.
    • This limits optimal performance in complex dynamic scenes.

    Purpose of the Study:

    • To develop a unified framework for joint stereo video deblurring, 3D scene flow estimation, and moving object segmentation.
    • To leverage intrinsic connections between these tasks for improved accuracy.
    • To address limitations of separate task-specific approaches.

    Main Methods:

    • A piece-wise planar model represents dynamic scenes, connecting structure, motion, and segmentation.
    • An energy minimization formulation integrates blur model constraints, moving objects, and 3D scene structure.
    • Joint optimization of deblurring, scene flow, and segmentation within a single framework.

    Main Results:

    • Significant improvements demonstrated in stereo video deblurring.
    • Enhanced accuracy in 3D scene flow estimation.
    • Superior performance in moving object segmentation compared to state-of-the-art methods.

    Conclusions:

    • The unified framework effectively exploits task interconnections for superior results.
    • The piece-wise planar model provides a robust representation for dynamic scenes.
    • The method shows strong performance on both synthetic and real-world datasets with challenging conditions.