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Uncertainty Learning for Noise Resistant Sketch-Based 3D Shape Retrieval.

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    This study introduces a novel approach to handle noisy sketch data in 3D shape retrieval. By modeling sketch noise as data uncertainty, the method significantly improves retrieval performance.

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    Area of Science:

    • Computer Graphics
    • Computer Vision
    • Machine Learning

    Background:

    • Sketch-based 3D shape retrieval is an emerging research area.
    • Existing methods primarily address the cross-modality gap between 2D sketches and 3D shapes.
    • The impact of noisy sketch data on retrieval performance has been largely overlooked.

    Purpose of the Study:

    • To investigate the problem of noisy sketch data in sketch-based 3D shape retrieval.
    • To propose a novel method for estimating and mitigating the effects of sketch noise.
    • To improve the robustness and accuracy of 3D shape retrieval systems using sketches.

    Main Methods:

    • Analysis of the impact of noise on sketch-based 3D shape retrieval performance.
    • Modeling sketch noise as data uncertainty using distributional representations.
    • Development of methods with simple network structures and loss functions for noise estimation.

    Main Results:

    • Noisy sketch data significantly impairs feature learning and causes overfitting, leading to unsatisfactory retrieval performance.
    • The proposed methods effectively estimate sketch noise as data uncertainty.
    • The approach establishes new state-of-the-art results on two benchmark datasets.

    Conclusions:

    • Addressing sketch noise is critical for improving sketch-based 3D shape retrieval.
    • Learning sketch features with uncertainty is crucial for noise-resistant retrieval.
    • The proposed methods offer an effective solution for handling noisy sketch data in 3D shape retrieval.