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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, K. Asari Vijayan

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium poster

Comments

Presenter: Quinn Robert Graehling

Deep Learning and Object Detection in 3D

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