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    We introduce the Hong Kong world model for analyzing scenes with slopes, improving upon Manhattan and Atlanta models. Our novel SLAM method uses this model for more accurate and efficient 3D scene reconstruction and camera pose estimation.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Geometric Modeling

    Background:

    • Existing models like Manhattan and Atlanta worlds effectively describe scenes with only vertical and horizontal dominant directions (DDs).
    • Representing scenes with additional sloping DDs using mixtures of Manhattan worlds can lead to unaligned and inaccurate results.
    • There is a need for a more general and accurate structural model for scenes with complex geometries, including slopes.

    Purpose of the Study:

    • To propose a novel structural model, the Hong Kong world, capable of representing environments with sloping dominant directions.
    • To develop a line-based Simultaneous Localization and Mapping (SLAM) method leveraging the Hong Kong world model for improved accuracy and efficiency.
    • To establish a new dataset of sequential images captured in a Hong Kong world environment.

    Main Methods:

    • Developed the Hong Kong world model, enforcing orthogonality constraints between vertical, horizontal, and sloping DDs for compactness and accuracy.
    • Implemented a novel semi-searching strategy with consensus voting for estimating DDs/vanishing points, determining their number, and achieving quasi-global optimality.
    • Computed camera pose by exploiting spatial relations between DDs within the Hong Kong world model, yielding concise polynomials for efficiency.
    • Refined estimated DDs using a filter-based method and optimized camera poses and 3D lines for enhanced accuracy and robustness.

    Main Results:

    • The Hong Kong world model provides a more general and accurate representation for scenes with slopes compared to existing models.
    • The proposed SLAM method demonstrates superior accuracy and efficiency in estimating DDs, camera poses, and 3D lines.
    • Experimental results show that the new approach outperforms state-of-the-art methods on the newly established Hong Kong world dataset.

    Conclusions:

    • The Hong Kong world model is a significant advancement for representing and analyzing complex 3D environments with slopes.
    • The novel line-based SLAM method offers a reliable and efficient solution for 3D reconstruction and localization in challenging environments.
    • The introduced dataset will facilitate future research in geometric computer vision and SLAM.