Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images

Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images

In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obta...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Holzer, S., Rusu, R. B., Dixon, M., Gedikli, S., Navab, N.
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Covariance matrix
Estimation
Noise
Sensors
Smoothing methods
Surface treatment
Vectors
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 2689
container_issue
container_start_page 2684
container_title
container_volume
creator Holzer, S.
Rusu, R. B.
Dixon, M.
Gedikli, S.
Navab, N.
description In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.
doi_str_mv 10.1109/IROS.2012.6385999
format Conference Proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_6385999</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6385999</ieee_id><sourcerecordid>6385999</sourcerecordid><originalsourceid>FETCH-LOGICAL-c289t-a84fe5dda0d414850c28e81b7a3c56f72a21e4e2ce1c01b3921d87824b876a453</originalsourceid><addsrcrecordid>eNo9kMlqwzAQhtUNmqZ5gNKLXsCpRrIs6RhCNwgEupyDYo0dFccKklNooe9eQdKeBr5_GfgJuQE2BWDm7vll-TrlDPi0EloaY07IxCgNZaUEKCHhlIw4SFEwXVVn5OpPUOL8X5D6kkxS-mCM5c5KgBmRn5mzu8F_Iu3Rt5t1iJsQHE3YYT340NMmRBrRdsXgt0jTPja2zuYQt7ajmDK1B18MWxpia3v_jY7ugu8HWndh76izg6X75PuWZohtzMkcazFdk4vGdgknxzsm7w_3b_OnYrF8fJ7PFkXNtRkKq8sGpXOWuRJKLVnGqGGtrKhl1ShuOWCJvEaoGayF4eC00rxca1XZUooxuT30ekRc7WL-Hr9WxyXFL43sZbI</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Holzer, S. ; Rusu, R. B. ; Dixon, M. ; Gedikli, S. ; Navab, N.</creator><creatorcontrib>Holzer, S. ; Rusu, R. B. ; Dixon, M. ; Gedikli, S. ; Navab, N.</creatorcontrib><description>In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.</description><identifier>ISSN: 2153-0858</identifier><identifier>ISBN: 1467317373</identifier><identifier>ISBN: 9781467317375</identifier><identifier>EISSN: 2153-0866</identifier><identifier>EISBN: 9781467317351</identifier><identifier>EISBN: 1467317365</identifier><identifier>EISBN: 9781467317368</identifier><identifier>EISBN: 1467317357</identifier><identifier>DOI: 10.1109/IROS.2012.6385999</identifier><language>eng</language><publisher>IEEE</publisher><subject>Covariance matrix ; Estimation ; Noise ; Sensors ; Smoothing methods ; Surface treatment ; Vectors</subject><ispartof>2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, p.2684-2689</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c289t-a84fe5dda0d414850c28e81b7a3c56f72a21e4e2ce1c01b3921d87824b876a453</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6385999$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6385999$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Holzer, S.</creatorcontrib><creatorcontrib>Rusu, R. B.</creatorcontrib><creatorcontrib>Dixon, M.</creatorcontrib><creatorcontrib>Gedikli, S.</creatorcontrib><creatorcontrib>Navab, N.</creatorcontrib><title>Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images</title><title>2012 IEEE/RSJ International Conference on Intelligent Robots and Systems</title><addtitle>IROS</addtitle><description>In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.</description><subject>Covariance matrix</subject><subject>Estimation</subject><subject>Noise</subject><subject>Sensors</subject><subject>Smoothing methods</subject><subject>Surface treatment</subject><subject>Vectors</subject><issn>2153-0858</issn><issn>2153-0866</issn><isbn>1467317373</isbn><isbn>9781467317375</isbn><isbn>9781467317351</isbn><isbn>1467317365</isbn><isbn>9781467317368</isbn><isbn>1467317357</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo9kMlqwzAQhtUNmqZ5gNKLXsCpRrIs6RhCNwgEupyDYo0dFccKklNooe9eQdKeBr5_GfgJuQE2BWDm7vll-TrlDPi0EloaY07IxCgNZaUEKCHhlIw4SFEwXVVn5OpPUOL8X5D6kkxS-mCM5c5KgBmRn5mzu8F_Iu3Rt5t1iJsQHE3YYT340NMmRBrRdsXgt0jTPja2zuYQt7ajmDK1B18MWxpia3v_jY7ugu8HWndh76izg6X75PuWZohtzMkcazFdk4vGdgknxzsm7w_3b_OnYrF8fJ7PFkXNtRkKq8sGpXOWuRJKLVnGqGGtrKhl1ShuOWCJvEaoGayF4eC00rxca1XZUooxuT30ekRc7WL-Hr9WxyXFL43sZbI</recordid><startdate>201210</startdate><enddate>201210</enddate><creator>Holzer, S.</creator><creator>Rusu, R. B.</creator><creator>Dixon, M.</creator><creator>Gedikli, S.</creator><creator>Navab, N.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201210</creationdate><title>Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images</title><author>Holzer, S. ; Rusu, R. B. ; Dixon, M. ; Gedikli, S. ; Navab, N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c289t-a84fe5dda0d414850c28e81b7a3c56f72a21e4e2ce1c01b3921d87824b876a453</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Covariance matrix</topic><topic>Estimation</topic><topic>Noise</topic><topic>Sensors</topic><topic>Smoothing methods</topic><topic>Surface treatment</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Holzer, S.</creatorcontrib><creatorcontrib>Rusu, R. B.</creatorcontrib><creatorcontrib>Dixon, M.</creatorcontrib><creatorcontrib>Gedikli, S.</creatorcontrib><creatorcontrib>Navab, N.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Holzer, S.</au><au>Rusu, R. B.</au><au>Dixon, M.</au><au>Gedikli, S.</au><au>Navab, N.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images</atitle><btitle>2012 IEEE/RSJ International Conference on Intelligent Robots and Systems</btitle><stitle>IROS</stitle><date>2012-10</date><risdate>2012</risdate><spage>2684</spage><epage>2689</epage><pages>2684-2689</pages><issn>2153-0858</issn><eissn>2153-0866</eissn><isbn>1467317373</isbn><isbn>9781467317375</isbn><eisbn>9781467317351</eisbn><eisbn>1467317365</eisbn><eisbn>9781467317368</eisbn><eisbn>1467317357</eisbn><abstract>In this paper we present two real-time methods for estimating surface normals from organized point cloud data. The proposed algorithms use integral images to perform highly efficient border- and depth-dependent smoothing and covariance estimation. We show that this approach makes it possible to obtain robust surface normals from large point clouds at high frame rates and therefore, can be used in real-time computer vision algorithms that make use of Kinect-like data.</abstract><pub>IEEE</pub><doi>10.1109/IROS.2012.6385999</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 2153-0858
ispartof 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, p.2684-2689
issn 2153-0858
2153-0866
language eng
recordid cdi_ieee_primary_6385999
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Covariance matrix
Estimation
Noise
Sensors
Smoothing methods
Surface treatment
Vectors
title Adaptive neighborhood selection for real-time surface normal estimation from organized point cloud data using integral images
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T21%3A32%3A39IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Adaptive%20neighborhood%20selection%20for%20real-time%20surface%20normal%20estimation%20from%20organized%20point%20cloud%20data%20using%20integral%20images&rft.btitle=2012%20IEEE/RSJ%20International%20Conference%20on%20Intelligent%20Robots%20and%20Systems&rft.au=Holzer,%20S.&rft.date=2012-10&rft.spage=2684&rft.epage=2689&rft.pages=2684-2689&rft.issn=2153-0858&rft.eissn=2153-0866&rft.isbn=1467317373&rft.isbn_list=9781467317375&rft_id=info:doi/10.1109/IROS.2012.6385999&rft_dat=%3Cieee_6IE%3E6385999%3C/ieee_6IE%3E%3Curl%3E%3C/url%3E&rft.eisbn=9781467317351&rft.eisbn_list=1467317365&rft.eisbn_list=9781467317368&rft.eisbn_list=1467317357&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6385999&rfr_iscdi=true