Presenter(s)

    Quinn Robert Graehling

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    Description

    The advent of deep learning for object detection has led to a wave of new ways for autonomous object labeling techniques for various applications such as autonomous driving and maneuvering, pedestrian/vehicle detection and target identification. Though most previous object detection techniques used RGB-D and 2D detection techniques, the recent increase in LiDar capabilities and point cloud generation has led to an interest in 3D object detection. This research takes a look at current 3D object detection and deep learning networks and conducts a performance comparison with their 2D counterparts.

    Publication Date

    4-24-2019

    Project Designation

    Independent Research

    Primary Advisor

    Theus H. Aspiras, Vijayan K. Asari

    Primary Advisor's Department

    Electrical and Computer Engineering

    Keywords

    Stander Symposium project

    Deep Learning and Object Detection in 3D

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