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    This study introduces a large-scale synthetic dataset for disaster scenarios, enabling advancements in computer vision and robotics for rescue operations. The dataset facilitates robust egocentric localization, improving safety and response in disaster situations.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Disaster Response

    Background:

    • Increasing demand for visual observation in disaster response.
    • Shortage of datasets for disaster scenarios hindering progress in computer vision and robotics.
    • Need for realistic data to train and evaluate AI models for rescue and safety.

    Purpose of the Study:

    • To present the first large-scale synthetic dataset of egocentric viewpoints for disaster scenarios.
    • To enable advancements in computer vision and robotics for disaster response.
    • To develop and evaluate robust egocentric localization methods for disaster environments.

    Main Methods:

    • Simulation of pre- and post-disaster scenarios with drastic appearance changes (fire, earthquakes).
    • Creation of over 300K high-resolution stereo image pairs with comprehensive annotations (semantic label, depth, optical flow, surface normal, camera poses).
    • Augmentation of realistic disaster scenes using 3D models and physically-based graphics.
    • Training and evaluation of state-of-the-art computer vision methods on the dataset.
    • Proposal of a novel convolutional neural network-based egocentric localization method robust to appearance and layout changes.

    Main Results:

    • The dataset facilitates training and evaluation of computer vision tasks in disaster scenarios.
    • State-of-the-art methods show improved recognition of disaster situations and reliable results.
    • The proposed egocentric localization method demonstrates robustness to drastic appearance and layout changes.
    • Experimental results confirm reliable camera pose predictions even in significantly altered conditions.

    Conclusions:

    • The developed synthetic dataset is crucial for advancing computer vision and robotics in disaster response.
    • The proposed egocentric localization method offers reliable performance in challenging, dynamic disaster environments.
    • This work provides a valuable resource and a robust solution for improving safety and rescue operations through AI.