東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

3D Object Detection via 2D LiDar Corrected Pseudo LiDar Point Clouds.

Record Type:	Electronic resources : Monograph/item
Title/Author:	3D Object Detection via 2D LiDar Corrected Pseudo LiDar Point Clouds./
Author:	Sonje, Saurabh Mahendra.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	63 p.
Notes:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28715863
ISBN:	9798538123827

3D Object Detection via 2D LiDar Corrected Pseudo LiDar Point Clouds.
Sonje, Saurabh Mahendra.

3D Object Detection via 2D LiDar Corrected Pseudo LiDar Point Clouds. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 63 p.

Source: Masters Abstracts International, Volume: 83-03.

Thesis (M.S.)--Rochester Institute of Technology, 2021.

This item must not be sold to any third party vendors.

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.

ISBN: 9798538123827Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

3D object detection

3D Object Detection via 2D LiDar Corrected Pseudo LiDar Point Clouds.
LDR:02664nmm a2200373 4500 001 2345031
005 20220531062340.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798538123827
035 $a (MiAaPQ)AAI28715863
035 $a AAI28715863
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sonje, Saurabh Mahendra. $3 3683888
245 1 0 $a 3D Object Detection via 2D LiDar Corrected Pseudo LiDar Point Clouds.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 63 p.
500 $a Source: Masters Abstracts International, Volume: 83-03.
500 $a Advisor: Lu, Guoyu.
502 $a Thesis (M.S.)--Rochester Institute of Technology, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0465.
650 4 $a Computer science. $3 523869
650 4 $a Computer engineering. $3 621879
650 4 $a Remote sensing. $3 535394
650 4 $a Artificial intelligence. $3 516317
653 $a 3D object detection
653 $a LiDar
653 $a Machine Learning
653 $a Computer Vision
690 $a 0984
690 $a 0464
690 $a 0800
690 $a 0799
710 2 $a Rochester Institute of Technology. $b Computer Engineering. $3 1018788
773 0 $t Masters Abstracts International $g 83-03.
790 $a 0465
791 $a M.S.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28715863