The age of automation has led to significant research in the field of Machine Learning and Computer Vision. Computer Vision tasks fundamentally rely on information from digital images, videos, texts and sensors to build intelligent systems. In recent times, deep neural networks combined with computer vision algorithms have been successful in developing 2D object detection methods with a potential to be applied in real-time systems. However, performing fast and accurate 3D object detection is still a challenging problem. The automotive industry is shifting gears towards building electric vehicles, connected cars, sustainable vehicles and is expected to have a high growth potential in the coming years. 3D object detection is a critical task for autonomous driving vehicles and robots as it helps moving objects in the scene to effectively plan their motion around other objects. 3D object detection tasks leverage image data from camera and/or 3D point clouds obtained from expensive 3D LiDAR sensors to achieve high detection accuracy. The 3D LiDAR sensor provides accurate depth information that is required to estimate the third dimension of the objects in the scene. Typically, a 64 beam LiDAR sensor mounted on a self-driving car cost around $75000. In this thesis, we propose a cost-effective approach for 3D object detection using a low-cost 2D LiDAR sensor. We collectively use the single beam point cloud data from 2D LiDAR for depth correction in pseudo-LiDAR. The proposed methods are tested on the KITTI 3D object detection dataset.

Library of Congress Subject Headings

Three-dimensional imaging; Optical radar--Data processing; Remote-sensing images--Data processing; Computer vision; Machine learning

Publication Date


Document Type


Student Type


Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering (KGCOE)


Guoyu Lu

Advisor/Committee Member

Andres Kwasinski

Advisor/Committee Member

Alexander Loui


RIT – Main Campus

Plan Codes