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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Robotics

    Background:

    • Text-guided 3-D visual grounding (T-3DVG) aims to identify objects in 3-D scenes based on natural language descriptions.
    • This task is crucial for human-robot interaction and autonomous systems but faces challenges in data collection and 3-D point cloud processing.

    Purpose of the Study:

    • To provide a comprehensive survey of the current state-of-the-art in T-3DVG.
    • To systematically review fundamental concepts, recent advancements, and future research avenues in the field.
    • To offer a tutorial-style overview of the T-3DVG pipeline and its components.

    Main Methods:

    • Categorization and analysis of existing T-3DVG approaches.
    • Review of benchmark datasets and evaluation metrics.
    • Discussion of current limitations and future research directions.

    Main Results:

    • The survey presents a structured overview of T-3DVG, categorizing existing methods and analyzing their performance.
    • It highlights the challenges and potential of T-3DVG compared to its 2-D counterpart.
    • Key datasets and evaluation metrics are summarized for performance assessment.

    Conclusions:

    • This work represents the first systematic survey dedicated to T-3DVG.
    • It identifies limitations in current T-3DVG research and proposes promising future research directions.
    • The survey aims to guide researchers and practitioners in this rapidly evolving field.