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3D Object Detection From Images for Autonomous Driving: A Survey.

Xinzhu Ma, Wanli Ouyang, Andrea Simonelli

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 25, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This paper surveys image-based 3D object detection for autonomous driving, organizing over 200 recent studies. It analyzes common methods, proposes new taxonomies, and discusses future research directions.

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    Area of Science:

    • Computer Vision
    • Robotics
    • Artificial Intelligence

    Background:

    • 3D object detection from images is crucial for autonomous driving.
    • Deep learning has driven significant progress in this field since 2015.
    • A comprehensive survey of recent advancements is currently lacking.

    Purpose of the Study:

    • To provide the first comprehensive survey of image-based 3D object detection.
    • To organize and analyze the vast body of research from 2015-2021.
    • To propose new taxonomies for systematic review and fair comparison of methods.

    Main Methods:

    • Comprehensive literature review of over 200 works (2015-2021).
    • Analysis of common pipelines and their components in image-based 3D detection.
    • Development of two novel taxonomies to categorize existing methods.

    Main Results:

    • Summarization of key theories, algorithms, and applications in the field.
    • Systematic organization of state-of-the-art methods using proposed taxonomies.
    • Identification of current challenges and future research avenues.

    Conclusions:

    • The survey fills a critical gap by consolidating knowledge on image-based 3D object detection.
    • The proposed taxonomies facilitate a structured understanding and comparison of methods.
    • The analysis of challenges and future directions guides ongoing research in autonomous driving technology.