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    Area of Science:

    • Computer Vision
    • Digital Architecture
    • Image Processing

    Background:

    • Large-scale architectural datasets are crucial for urban modeling and digital heritage.
    • Extracting specific architectural elements from panoramic imagery presents significant challenges.
    • Automated methods are needed to efficiently process vast visual data collections.

    Purpose of the Study:

    • To develop an automated system for extracting architectural assets from panoramic imagery.
    • To enable attribute identification for asset quality assessment and search indexing.
    • To provide tools for visualization and querying of extracted architectural data.

    Main Methods:

    • Automated rectification and cropping of dominant planes in panoramic images.
    • Object detection algorithms for identifying architectural assets (e.g., façades, windows).
    • Development of tools for attribute identification, quality assessment, and asset indexing.
    • Creation of a user interface for asset visualization and querying.

    Main Results:

    • Successful extraction of architectural assets from large-scale panoramic image collections.
    • Implementation of tools for determining asset quality and enabling efficient search.
    • Demonstration of applications in urban modeling and texture synthesis.

    Conclusions:

    • The developed system offers an efficient method for architectural asset extraction from panoramic imagery.
    • Automated analysis enhances the usability of large visual datasets for architectural research and applications.
    • The system facilitates urban modeling and texture synthesis through structured asset data.