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Epipolar Geometry Estimation for Urban Scenes with Repetitive Structures.

Maria Kushnir, Ilan Shimshoni

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 10, 2015
    PubMed
    Summary

    This study introduces a novel algorithm for estimating epipolar geometry in images with repeated structures, like building facades. It effectively matches features in challenging wide baseline images, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Photogrammetry

    Background:

    • Estimating epipolar geometry is crucial for 3D scene reconstruction.
    • Existing algorithms struggle with wide baseline images containing repetitive structures, such as building facades.
    • Local feature matching fails when repetitive structures lack unique characteristics.

    Purpose of the Study:

    • To develop a robust algorithm for estimating epipolar geometry from images with repeated structures.
    • To address the limitations of current methods in handling facade-like scenes.
    • To accurately match features in challenging wide baseline image pairs.

    Main Methods:

    • Image rectification to create a fronto-parallel facade view.
    • Clustering of similar features within each image.
    • Matching feature clusters to generate hypothesized homographies.
    • Epipole recovery to estimate the fundamental matrix.
    • Reliability check for fundamental matrix estimation, defaulting to homography.

    Main Results:

    • The proposed algorithm successfully handles scenes with repeated structures, a common challenge in computer vision.
    • It outperforms several state-of-the-art algorithms on the ZuBuD benchmark dataset.
    • The method demonstrates robustness in estimating epipolar geometry even with significant baseline differences.

    Conclusions:

    • The developed algorithm provides a reliable solution for epipolar geometry estimation in scenes with repetitive structures.
    • It offers improved performance over existing methods, particularly for facade-based image pairs.
    • The approach enhances the accuracy and applicability of wide baseline stereo vision.