Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorChen, Xiaoxinen_US
dc.identifier.citationChen, X. (2022). Machine learning for object identification using Lidar point cloud data. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractDue to the increasing number of point cloud applications in computer vision and autonomous driving, more research attention has been focused on 3D point cloud learning. With the dominant approach in solving 2D image problems, deep learning is the most frequent model used in 3D point cloud processing. However, deep learning on point clouds is still in its infancy due to the specific characteristics of point clouds, such as permutation invariance. Nowadays, numerous methods applied deep learning on point cloud have been proposed to address the difficulties. This study provides a detailed but comprehensive analysis of recent developments in deep learning methods for 3D point cloud object classification in order to motivate future research. It also includes standardized and integrated practical codes with validation and visualization to provide researchers with convenience in understanding and evaluating the frameworks. Insightful discussion based on the comparative experiment results from the benchmark and real-life LiDAR datasets may further give inspiration on future research directions.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleMachine learning for object identification using Lidar point cloud dataen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorMao Kezhien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
dc.contributor.organizationInstitute of High Performance Computingen_US
dc.contributor.supervisor2Yang Fengen_US
item.fulltextWith Fulltext-
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
FYP_Final_Report_Chen Xiaoxin.pdf
  Restricted Access
4.18 MBAdobe PDFView/Open

Page view(s)

Updated on Mar 4, 2024


Updated on Mar 4, 2024

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.