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Resolving Symmetry Ambiguity in Correspondence-Based Methods for Instance-Level Object Pose Estimation.

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    This study introduces SymCode and SymNet for estimating object pose from images, especially for symmetric objects. The new method uses one-to-many correspondences to overcome ambiguity and achieves fast, accurate results.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Machine Learning

    Background:

    • Estimating object pose from RGB images is challenging, particularly for symmetric objects.
    • Existing methods using one-to-one pixel-to-vertex correspondences struggle with symmetry-induced ambiguity.

    Purpose of the Study:

    • To develop a novel approach for accurate 6D object pose estimation of symmetric objects.
    • To address the ambiguity issues inherent in one-to-one correspondence methods.

    Main Methods:

    • Proposed SymCode, a symmetry-aware surface encoding using one-to-many correspondences.
    • Introduced SymNet, an end-to-end network for direct 6D pose regression, bypassing PnP solvers.

    Main Results:

    • SymNet demonstrated faster runtime compared to existing methods.
    • Achieved comparable accuracy on the T-LESS and IC-BIN benchmarks, which feature many symmetric objects.

    Conclusions:

    • The proposed SymCode and SymNet effectively handle pose estimation for symmetric objects.
    • The method offers an efficient and accurate alternative for 6D pose estimation tasks.