As an important application of Artificial Intelligence (AI), autonomous driving has developed rapidly in recent years. 3D object detection in autonomous driving has attracted more and more attention because it provides precise range and size information of an object. Some of the detection algorithms rely on point cloud data acquired from LiDAR. Compared with LiDAR, the optical image solution for autonomous driving is cheaper, so that image-based detection approaches are also popular. The chapter overview the recent advance on 3D object detection using 3D point cloud or image directly. For the computing simplicity, the approaches which detect 3D object from the input images without 3D point reconstruction benefit the vehicle computational platform. However, the object location accuracy of such approaches is limited. Therefore, a proposed method named as Descriptor Enhanced Stereo R-CNN (DESR-CNN) is specified in detail. Several 3D object detection algorithms are tested on KITTI dataset. The experimental results demonstrate that DESR-CNN outperforms most of the existing 3D object detection methods based on binocular images.


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    Title :

    3D Object Detection for Autonomous Driving


    Additional title:

    Lecture Notes in Intelligent Transportation and Infrastructure


    Contributors:
    Murphey, Yi Lu (editor) / Kolmanovsky, Ilya (editor) / Watta, Paul (editor) / Tan, Yihua (author) / Chen, Siwei (author) / Yan, Pei (author)


    Publication date :

    2022-09-08


    Size :

    17 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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