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Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Guobao Xiao, Jun Yu, Jiayi Ma

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 13, 2024
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    Summary
    This summary is machine-generated.

    Estimating geometric model parameters with outliers is challenging. Latent Semantic Consensus (LSC) effectively removes outliers and estimates models, offering superior accuracy and speed for computer vision tasks.

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    Area of Science:

    • Computer Vision
    • Geometric Modeling
    • Machine Learning

    Background:

    • Robust estimation of geometric model parameters from data with severe outliers is a critical challenge in computer vision.
    • Existing methods often struggle with multi-structural data, leading to inaccurate parameter estimations.
    • The need for efficient and accurate methods for handling outliers in model fitting is paramount.

    Purpose of the Study:

    • To propose an effective method, Latent Semantic Consensus (LSC), for estimating geometric model parameters from multi-structural data containing severe outliers.
    • To preserve latent semantic consensus in both data points and model hypotheses for improved model fitting.
    • To achieve accurate and efficient multi-structural model fitting.

    Main Methods:

    • Formulated the model fitting problem into two latent semantic spaces: one for data points and one for model hypotheses.
    • Explored the distributions of points within these latent spaces to identify and remove outliers.
    • Generated high-quality model hypotheses and effectively estimated model instances using the LSC principle.

    Main Results:

    • Latent Semantic Consensus (LSC) demonstrated significant superiority in both accuracy and speed compared to state-of-the-art methods.
    • LSC provides consistent and reliable solutions for general multi-structural model fitting.
    • The method achieves its performance within milliseconds, highlighting its efficiency on synthetic and real-world image data.

    Conclusions:

    • Latent Semantic Consensus (LSC) is a highly effective and efficient method for robust geometric model parameter estimation in the presence of severe outliers.
    • The LSC approach offers a promising solution for challenging computer vision problems involving multi-structural data.
    • The deterministic nature and efficiency of LSC make it suitable for real-time applications.