Loading…

A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving

•A comprehensive survey of LIDAR-based 3D object detection methods with Deep learning for autonomous driving•Presentation of a common operational pipeline that sets the base for a structured categorisation which facilitates comparison and emerges the similarities and dissimilarities among the presen...

Full description

Saved in:
Bibliographic Details
Published in:Computers & graphics 2021-10, Vol.99, p.153-181
Main Authors: Zamanakos, Georgios, Tsochatzidis, Lazaros, Amanatiadis, Angelos, Pratikakis, Ioannis
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c325t-ed976a3a62891789d22098ea08401dad4a854446e1f64a741f0461b2ae6b2a933
cites cdi_FETCH-LOGICAL-c325t-ed976a3a62891789d22098ea08401dad4a854446e1f64a741f0461b2ae6b2a933
container_end_page 181
container_issue
container_start_page 153
container_title Computers & graphics
container_volume 99
creator Zamanakos, Georgios
Tsochatzidis, Lazaros
Amanatiadis, Angelos
Pratikakis, Ioannis
description •A comprehensive survey of LIDAR-based 3D object detection methods with Deep learning for autonomous driving•Presentation of a common operational pipeline that sets the base for a structured categorisation which facilitates comparison and emerges the similarities and dissimilarities among the presented methods.•Presentation of an exhaustive up-to-date list of 3D object detectors. The method’s description focuses on the particularities of each method with respect to the operational pipeline identifying pros and cons towards an effective and efficient detection outcome.•Fruitful discussion that aims to identify key features which should be either adopted or avoided in the design of new 3D object detectors. [Display omitted] LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of both academia and industry since it relies upon a sensor that incorporates appealing features like insensitivity to light and capacity to capture the 3D spatial structure of an object along with the continuous reduction of its purchase cost. Furthermore, the emergence of Deep Learning as the means to boost performance in 3D data analysis stimulated the production of a multitude of solutions for LIDAR-based 3D object detection which followed different approaches in an effort to respond effectively to several challenges. In view of this, this paper presents a comprehensive survey of LIDAR-based 3D object detection methods wherein an analysis of existing methods is addressed by taking into account a new categorisation that relies upon a common operational pipeline which describes the end-to-end functionality of each method. We next, discuss the existing benchmarking frameworks and present the performance achieved by each method in each of them. Finally, a discussion is presented that provides key insights aiming to capture the essence of current trends in LIDAR-based 3D object detection.
doi_str_mv 10.1016/j.cag.2021.07.003
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2599939911</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0097849321001321</els_id><sourcerecordid>2599939911</sourcerecordid><originalsourceid>FETCH-LOGICAL-c325t-ed976a3a62891789d22098ea08401dad4a854446e1f64a741f0461b2ae6b2a933</originalsourceid><addsrcrecordid>eNp9kM1LAzEQxYMoWKt_gLeA510nH81u8FT8hoIgeg7pZrbN2m5qslvxvzdSz17mweO9meFHyCWDkgFT113Z2FXJgbMSqhJAHJEJqytRVKqWx2QCoKuillqckrOUOgDgXMkJ-ZjTJmx3EdfYJ79Hmsa4x28aWrp4vpu_Fkub0FFxR8Oyw2agDocsPvR0i8M6uES__LDONu7oBm3sfb-ibYjUjkPowzaMibro99k-Jyet3SS8-NMpeX-4f7t9KhYvj8-380XRCD4bCnS6UlZYxWvNqlo7zkHXaKGWwJx10tYzKaVC1ippK8lakIotuUWVhxZiSq4Oe3cxfI6YBtOFMfb5pOEzrbXQmrGcYodUE0NKEVuzi35r47dhYH6Zms5kpuaXqYHKZKa5c3PoYH5_7zGa1HjsG3Q-ZirGBf9P-wfjuH6A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2599939911</pqid></control><display><type>article</type><title>A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving</title><source>Elsevier</source><creator>Zamanakos, Georgios ; Tsochatzidis, Lazaros ; Amanatiadis, Angelos ; Pratikakis, Ioannis</creator><creatorcontrib>Zamanakos, Georgios ; Tsochatzidis, Lazaros ; Amanatiadis, Angelos ; Pratikakis, Ioannis</creatorcontrib><description>•A comprehensive survey of LIDAR-based 3D object detection methods with Deep learning for autonomous driving•Presentation of a common operational pipeline that sets the base for a structured categorisation which facilitates comparison and emerges the similarities and dissimilarities among the presented methods.•Presentation of an exhaustive up-to-date list of 3D object detectors. The method’s description focuses on the particularities of each method with respect to the operational pipeline identifying pros and cons towards an effective and efficient detection outcome.•Fruitful discussion that aims to identify key features which should be either adopted or avoided in the design of new 3D object detectors. [Display omitted] LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of both academia and industry since it relies upon a sensor that incorporates appealing features like insensitivity to light and capacity to capture the 3D spatial structure of an object along with the continuous reduction of its purchase cost. Furthermore, the emergence of Deep Learning as the means to boost performance in 3D data analysis stimulated the production of a multitude of solutions for LIDAR-based 3D object detection which followed different approaches in an effort to respond effectively to several challenges. In view of this, this paper presents a comprehensive survey of LIDAR-based 3D object detection methods wherein an analysis of existing methods is addressed by taking into account a new categorisation that relies upon a common operational pipeline which describes the end-to-end functionality of each method. We next, discuss the existing benchmarking frameworks and present the performance achieved by each method in each of them. Finally, a discussion is presented that provides key insights aiming to capture the essence of current trends in LIDAR-based 3D object detection.</description><identifier>ISSN: 0097-8493</identifier><identifier>EISSN: 1873-7684</identifier><identifier>DOI: 10.1016/j.cag.2021.07.003</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>3D Object detection ; Autonomous driving ; Data analysis ; Deep learning ; Lidar ; Object recognition</subject><ispartof>Computers &amp; graphics, 2021-10, Vol.99, p.153-181</ispartof><rights>2021 Elsevier Ltd</rights><rights>Copyright Elsevier Science Ltd. Oct 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-ed976a3a62891789d22098ea08401dad4a854446e1f64a741f0461b2ae6b2a933</citedby><cites>FETCH-LOGICAL-c325t-ed976a3a62891789d22098ea08401dad4a854446e1f64a741f0461b2ae6b2a933</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Zamanakos, Georgios</creatorcontrib><creatorcontrib>Tsochatzidis, Lazaros</creatorcontrib><creatorcontrib>Amanatiadis, Angelos</creatorcontrib><creatorcontrib>Pratikakis, Ioannis</creatorcontrib><title>A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving</title><title>Computers &amp; graphics</title><description>•A comprehensive survey of LIDAR-based 3D object detection methods with Deep learning for autonomous driving•Presentation of a common operational pipeline that sets the base for a structured categorisation which facilitates comparison and emerges the similarities and dissimilarities among the presented methods.•Presentation of an exhaustive up-to-date list of 3D object detectors. The method’s description focuses on the particularities of each method with respect to the operational pipeline identifying pros and cons towards an effective and efficient detection outcome.•Fruitful discussion that aims to identify key features which should be either adopted or avoided in the design of new 3D object detectors. [Display omitted] LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of both academia and industry since it relies upon a sensor that incorporates appealing features like insensitivity to light and capacity to capture the 3D spatial structure of an object along with the continuous reduction of its purchase cost. Furthermore, the emergence of Deep Learning as the means to boost performance in 3D data analysis stimulated the production of a multitude of solutions for LIDAR-based 3D object detection which followed different approaches in an effort to respond effectively to several challenges. In view of this, this paper presents a comprehensive survey of LIDAR-based 3D object detection methods wherein an analysis of existing methods is addressed by taking into account a new categorisation that relies upon a common operational pipeline which describes the end-to-end functionality of each method. We next, discuss the existing benchmarking frameworks and present the performance achieved by each method in each of them. Finally, a discussion is presented that provides key insights aiming to capture the essence of current trends in LIDAR-based 3D object detection.</description><subject>3D Object detection</subject><subject>Autonomous driving</subject><subject>Data analysis</subject><subject>Deep learning</subject><subject>Lidar</subject><subject>Object recognition</subject><issn>0097-8493</issn><issn>1873-7684</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kM1LAzEQxYMoWKt_gLeA510nH81u8FT8hoIgeg7pZrbN2m5qslvxvzdSz17mweO9meFHyCWDkgFT113Z2FXJgbMSqhJAHJEJqytRVKqWx2QCoKuillqckrOUOgDgXMkJ-ZjTJmx3EdfYJ79Hmsa4x28aWrp4vpu_Fkub0FFxR8Oyw2agDocsPvR0i8M6uES__LDONu7oBm3sfb-ibYjUjkPowzaMibro99k-Jyet3SS8-NMpeX-4f7t9KhYvj8-380XRCD4bCnS6UlZYxWvNqlo7zkHXaKGWwJx10tYzKaVC1ippK8lakIotuUWVhxZiSq4Oe3cxfI6YBtOFMfb5pOEzrbXQmrGcYodUE0NKEVuzi35r47dhYH6Zms5kpuaXqYHKZKa5c3PoYH5_7zGa1HjsG3Q-ZirGBf9P-wfjuH6A</recordid><startdate>202110</startdate><enddate>202110</enddate><creator>Zamanakos, Georgios</creator><creator>Tsochatzidis, Lazaros</creator><creator>Amanatiadis, Angelos</creator><creator>Pratikakis, Ioannis</creator><general>Elsevier Ltd</general><general>Elsevier Science Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>202110</creationdate><title>A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving</title><author>Zamanakos, Georgios ; Tsochatzidis, Lazaros ; Amanatiadis, Angelos ; Pratikakis, Ioannis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-ed976a3a62891789d22098ea08401dad4a854446e1f64a741f0461b2ae6b2a933</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>3D Object detection</topic><topic>Autonomous driving</topic><topic>Data analysis</topic><topic>Deep learning</topic><topic>Lidar</topic><topic>Object recognition</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zamanakos, Georgios</creatorcontrib><creatorcontrib>Tsochatzidis, Lazaros</creatorcontrib><creatorcontrib>Amanatiadis, Angelos</creatorcontrib><creatorcontrib>Pratikakis, Ioannis</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers &amp; graphics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zamanakos, Georgios</au><au>Tsochatzidis, Lazaros</au><au>Amanatiadis, Angelos</au><au>Pratikakis, Ioannis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving</atitle><jtitle>Computers &amp; graphics</jtitle><date>2021-10</date><risdate>2021</risdate><volume>99</volume><spage>153</spage><epage>181</epage><pages>153-181</pages><issn>0097-8493</issn><eissn>1873-7684</eissn><abstract>•A comprehensive survey of LIDAR-based 3D object detection methods with Deep learning for autonomous driving•Presentation of a common operational pipeline that sets the base for a structured categorisation which facilitates comparison and emerges the similarities and dissimilarities among the presented methods.•Presentation of an exhaustive up-to-date list of 3D object detectors. The method’s description focuses on the particularities of each method with respect to the operational pipeline identifying pros and cons towards an effective and efficient detection outcome.•Fruitful discussion that aims to identify key features which should be either adopted or avoided in the design of new 3D object detectors. [Display omitted] LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of both academia and industry since it relies upon a sensor that incorporates appealing features like insensitivity to light and capacity to capture the 3D spatial structure of an object along with the continuous reduction of its purchase cost. Furthermore, the emergence of Deep Learning as the means to boost performance in 3D data analysis stimulated the production of a multitude of solutions for LIDAR-based 3D object detection which followed different approaches in an effort to respond effectively to several challenges. In view of this, this paper presents a comprehensive survey of LIDAR-based 3D object detection methods wherein an analysis of existing methods is addressed by taking into account a new categorisation that relies upon a common operational pipeline which describes the end-to-end functionality of each method. We next, discuss the existing benchmarking frameworks and present the performance achieved by each method in each of them. Finally, a discussion is presented that provides key insights aiming to capture the essence of current trends in LIDAR-based 3D object detection.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.cag.2021.07.003</doi><tpages>29</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0097-8493
ispartof Computers & graphics, 2021-10, Vol.99, p.153-181
issn 0097-8493
1873-7684
language eng
recordid cdi_proquest_journals_2599939911
source Elsevier
subjects 3D Object detection
Autonomous driving
Data analysis
Deep learning
Lidar
Object recognition
title A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T12%3A16%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20comprehensive%20survey%20of%20LIDAR-based%203D%20object%20detection%20methods%20with%20deep%20learning%20for%20autonomous%20driving&rft.jtitle=Computers%20&%20graphics&rft.au=Zamanakos,%20Georgios&rft.date=2021-10&rft.volume=99&rft.spage=153&rft.epage=181&rft.pages=153-181&rft.issn=0097-8493&rft.eissn=1873-7684&rft_id=info:doi/10.1016/j.cag.2021.07.003&rft_dat=%3Cproquest_cross%3E2599939911%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c325t-ed976a3a62891789d22098ea08401dad4a854446e1f64a741f0461b2ae6b2a933%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2599939911&rft_id=info:pmid/&rfr_iscdi=true