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Domain-Translated 3D Object Pose Estimation.

Christos Papaioannidis, Vasileios Mygdalis, Ioannis Pitas

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 25, 2020
    PubMed
    Summary
    This summary is machine-generated.

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    This study introduces a new framework for 3D object pose estimation. It translates real-world images into synthetic ones, improving accuracy without needing real training data.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Robotics

    Background:

    • Synthetic 3D models are vital for object pose estimation, generating annotated data.
    • Current methods enrich real-world training data with synthetic images but ignore domain differences.
    • Real and synthetic image data distributions often differ, impacting pose estimation performance.

    Purpose of the Study:

    • To propose a novel framework for 3D object pose estimation that leverages synthetic data more effectively.
    • To address the domain gap between real and synthetic images in object pose estimation tasks.
    • To develop a method that improves 3D object pose estimation accuracy by translating real images to a synthetic domain.

    Main Methods:

    • A two-step framework is proposed: 1) pose-oriented image-to-image translation, 2) 3D object pose estimation.

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  • The image-to-image translation uses a novel objective function to preserve pose-relevant characteristics.
  • The pose estimation is performed on the translated synthetic images, eliminating the need for real training data.

    • Main Results:

    • The proposed framework significantly enhances 3D object pose estimation performance.
    • Experimental results demonstrate superior performance compared to existing state-of-the-art methods.
    • The pose-oriented translation effectively bridges the domain gap between real and synthetic images.

    Conclusions:

    • The novel framework offers a more effective approach to 3D object pose estimation by utilizing synthetic data.
    • Translating real images to synthetic ones with a pose-oriented objective improves estimation accuracy.
    • The method achieves high performance without requiring real data for training the pose estimation network.