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A Data-Driven Point Cloud Simplification Framework for City-Scale Image-Based Localization.

Wentao Cheng, Weisi Lin, Xinfeng Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 11, 2016
    PubMed
    Summary

    This study introduces a novel framework for simplifying large 3D point clouds, making image-based localization feasible on memory-constrained devices. The method effectively reduces data size while maintaining localization accuracy.

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    Area of Science:

    • Computer Vision
    • Geometric Computing
    • Robotics

    Background:

    • City-scale 3D point clouds are crucial for image-based localization but pose memory challenges.
    • Existing point cloud simplification methods struggle to balance data reduction with localization performance.

    Purpose of the Study:

    • To develop a data-driven framework for efficient 3D point cloud simplification for image-based localization.
    • To enable accurate 6-DOF camera pose estimation on devices with limited memory.

    Main Methods:

    • Formulated point cloud simplification as a weighted K-Cover problem.
    • Proposed a utility-based parameter determination for K-Cover.
    • Introduced an adaptive exponential weight function based on point visibility probability.

    Main Results:

    • The proposed framework significantly outperforms state-of-the-art methods in image-based localization.
    • Achieved comparable localization performance with reduced point cloud size.
    • Demonstrated effectiveness on three popular datasets.

    Conclusions:

    • The developed framework offers an effective solution for memory-efficient 3D point cloud simplification.
    • This approach enhances the feasibility of image-based localization in resource-constrained environments.
    • The weighted K-Cover formulation with adaptive weights proves superior for this task.