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Real-Time Globally Consistent 3D Reconstruction With Semantic Priors.

Shi-Sheng Huang, Haoxiang Chen, Jiahui Huang

    IEEE Transactions on Visualization and Computer Graphics
    |December 23, 2021
    PubMed
    Summary

    This study introduces a new real-time 3D reconstruction method integrating semantic and geometric data for improved global consistency in indoor scenes. The approach enhances accuracy by mapping semantic information into a measurable metric space.

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    Area of Science:

    • Computer Vision
    • Robotics
    • 3D Scene Reconstruction

    Background:

    • Global consistency is crucial for 3D indoor scene reconstruction.
    • Existing methods using geometric analysis struggle with satisfactory global consistency.

    Purpose of the Study:

    • To propose a novel real-time 3D reconstruction approach integrating semantic and geometric cues.
    • To address the challenge of mapping semantic priors into a metric space for accurate fusion.

    Main Methods:

    • Introduction of a semantic space with a continuous metric function.
    • Development of a frame-to-model semantic tracker for camera pose estimation.
    • Implementation of a semantic pose graph with semantic links for global consistency.

    Main Results:

    • The proposed approach effectively integrates semantic and geometric information.
    • Demonstrated superior performance in quantitative and qualitative evaluations on public datasets.
    • Achieved enhanced global consistency in 3D indoor scene reconstruction.

    Conclusions:

    • The novel approach significantly improves 3D reconstruction accuracy and global consistency.
    • Effective semantic-geometric fusion is achieved by leveraging a semantic space.
    • Outperforms previous methods in real-time 3D indoor scene reconstruction tasks.