Resection-Intersection Bundle Adjustment Revisited
Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resul...
Gespeichert in:
Veröffentlicht in: | ISRN machine vision 2013-12, Vol.2013, p.1-8 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 8 |
---|---|
container_issue | |
container_start_page | 1 |
container_title | ISRN machine vision |
container_volume | 2013 |
creator | Lakemond, Ruan Fookes, Clinton Sridharan, Sridha |
description | Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resulting in a fast and accurate approach. Firstly, a linear triangulation step is added to the intersection stage, yielding higher accuracy and improved convergence rate. Secondly, the effect of parameter updates is tracked in order to reduce wasteful computation; only variables coupled to significantly changing variables are updated. This leads to significant improvements in computation time, at the cost of a small, controllable increase in error. Loop closures are handled effectively without the need for additional network modelling. The proposed approach is shown experimentally to yield comparable accuracy to a full sparse bundle adjustment (20% error increase) while computation time scales much better with the number of variables. Experiments on a progressive reconstruction system show the proposed method to be more efficient by a factor of 65 to 177, and 4.5 times more accurate (increasing over time) than a localised sparse bundle adjustment approach. |
doi_str_mv | 10.1155/2013/261956 |
format | Article |
fullrecord | <record><control><sourceid>crossref_hinda</sourceid><recordid>TN_cdi_crossref_primary_10_1155_2013_261956</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_1155_2013_261956</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1506-b0363f90730fd9c36563bbfbd91a22135c54eeb0b627660e96052ae19725f643</originalsourceid><addsrcrecordid>eNp9j01rAjEQhkOpULGe-gf23LJ1JtnMmqOVfghCQTx4WzbJhEZ0LZu1pf--ynroqXN554WHFx4h7hAeEbWeSEA1kYRG05UYSjCQl1PYXP_5b8Q4pS2cblpCgTQUcsWJXRcPTb5oOm4vJXs6Nn7H2cxvj6nbc9NlK_6KKXbsb8Ug1LvE40uOxPrleT1_y5fvr4v5bJk71EC5BUUqGCgVBG-cIk3K2mC9wVpKVNrpgtmCJVkSARsCLWtGU0odqFAj8dDPuvaQUsuh-mzjvm5_KoTqLFydhate-ETf9_RHbHz9Hf-FfwFtklOQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Resection-Intersection Bundle Adjustment Revisited</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Lakemond, Ruan ; Fookes, Clinton ; Sridharan, Sridha</creator><contributor>Pardàs, M. ; Gasteratos, A.</contributor><creatorcontrib>Lakemond, Ruan ; Fookes, Clinton ; Sridharan, Sridha ; Pardàs, M. ; Gasteratos, A.</creatorcontrib><description>Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resulting in a fast and accurate approach. Firstly, a linear triangulation step is added to the intersection stage, yielding higher accuracy and improved convergence rate. Secondly, the effect of parameter updates is tracked in order to reduce wasteful computation; only variables coupled to significantly changing variables are updated. This leads to significant improvements in computation time, at the cost of a small, controllable increase in error. Loop closures are handled effectively without the need for additional network modelling. The proposed approach is shown experimentally to yield comparable accuracy to a full sparse bundle adjustment (20% error increase) while computation time scales much better with the number of variables. Experiments on a progressive reconstruction system show the proposed method to be more efficient by a factor of 65 to 177, and 4.5 times more accurate (increasing over time) than a localised sparse bundle adjustment approach.</description><identifier>ISSN: 2090-780X</identifier><identifier>EISSN: 2090-780X</identifier><identifier>DOI: 10.1155/2013/261956</identifier><language>eng</language><publisher>Hindawi Publishing Corporation</publisher><ispartof>ISRN machine vision, 2013-12, Vol.2013, p.1-8</ispartof><rights>Copyright © 2013 Ruan Lakemond et al.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1506-b0363f90730fd9c36563bbfbd91a22135c54eeb0b627660e96052ae19725f643</citedby><cites>FETCH-LOGICAL-c1506-b0363f90730fd9c36563bbfbd91a22135c54eeb0b627660e96052ae19725f643</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Pardàs, M.</contributor><contributor>Gasteratos, A.</contributor><creatorcontrib>Lakemond, Ruan</creatorcontrib><creatorcontrib>Fookes, Clinton</creatorcontrib><creatorcontrib>Sridharan, Sridha</creatorcontrib><title>Resection-Intersection Bundle Adjustment Revisited</title><title>ISRN machine vision</title><description>Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resulting in a fast and accurate approach. Firstly, a linear triangulation step is added to the intersection stage, yielding higher accuracy and improved convergence rate. Secondly, the effect of parameter updates is tracked in order to reduce wasteful computation; only variables coupled to significantly changing variables are updated. This leads to significant improvements in computation time, at the cost of a small, controllable increase in error. Loop closures are handled effectively without the need for additional network modelling. The proposed approach is shown experimentally to yield comparable accuracy to a full sparse bundle adjustment (20% error increase) while computation time scales much better with the number of variables. Experiments on a progressive reconstruction system show the proposed method to be more efficient by a factor of 65 to 177, and 4.5 times more accurate (increasing over time) than a localised sparse bundle adjustment approach.</description><issn>2090-780X</issn><issn>2090-780X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><recordid>eNp9j01rAjEQhkOpULGe-gf23LJ1JtnMmqOVfghCQTx4WzbJhEZ0LZu1pf--ynroqXN554WHFx4h7hAeEbWeSEA1kYRG05UYSjCQl1PYXP_5b8Q4pS2cblpCgTQUcsWJXRcPTb5oOm4vJXs6Nn7H2cxvj6nbc9NlK_6KKXbsb8Ug1LvE40uOxPrleT1_y5fvr4v5bJk71EC5BUUqGCgVBG-cIk3K2mC9wVpKVNrpgtmCJVkSARsCLWtGU0odqFAj8dDPuvaQUsuh-mzjvm5_KoTqLFydhate-ETf9_RHbHz9Hf-FfwFtklOQ</recordid><startdate>20131212</startdate><enddate>20131212</enddate><creator>Lakemond, Ruan</creator><creator>Fookes, Clinton</creator><creator>Sridharan, Sridha</creator><general>Hindawi Publishing Corporation</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20131212</creationdate><title>Resection-Intersection Bundle Adjustment Revisited</title><author>Lakemond, Ruan ; Fookes, Clinton ; Sridharan, Sridha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1506-b0363f90730fd9c36563bbfbd91a22135c54eeb0b627660e96052ae19725f643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lakemond, Ruan</creatorcontrib><creatorcontrib>Fookes, Clinton</creatorcontrib><creatorcontrib>Sridharan, Sridha</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><jtitle>ISRN machine vision</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lakemond, Ruan</au><au>Fookes, Clinton</au><au>Sridharan, Sridha</au><au>Pardàs, M.</au><au>Gasteratos, A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Resection-Intersection Bundle Adjustment Revisited</atitle><jtitle>ISRN machine vision</jtitle><date>2013-12-12</date><risdate>2013</risdate><volume>2013</volume><spage>1</spage><epage>8</epage><pages>1-8</pages><issn>2090-780X</issn><eissn>2090-780X</eissn><abstract>Bundle adjustment is one of the essential components of the computer vision toolbox. This paper revisits the resection-intersection approach, which has previously been shown to have inferior convergence properties. Modifications are proposed that greatly improve the performance of this method, resulting in a fast and accurate approach. Firstly, a linear triangulation step is added to the intersection stage, yielding higher accuracy and improved convergence rate. Secondly, the effect of parameter updates is tracked in order to reduce wasteful computation; only variables coupled to significantly changing variables are updated. This leads to significant improvements in computation time, at the cost of a small, controllable increase in error. Loop closures are handled effectively without the need for additional network modelling. The proposed approach is shown experimentally to yield comparable accuracy to a full sparse bundle adjustment (20% error increase) while computation time scales much better with the number of variables. Experiments on a progressive reconstruction system show the proposed method to be more efficient by a factor of 65 to 177, and 4.5 times more accurate (increasing over time) than a localised sparse bundle adjustment approach.</abstract><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2013/261956</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2090-780X |
ispartof | ISRN machine vision, 2013-12, Vol.2013, p.1-8 |
issn | 2090-780X 2090-780X |
language | eng |
recordid | cdi_crossref_primary_10_1155_2013_261956 |
source | EZB-FREE-00999 freely available EZB journals |
title | Resection-Intersection Bundle Adjustment Revisited |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T16%3A13%3A25IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_hinda&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Resection-Intersection%20Bundle%20Adjustment%20Revisited&rft.jtitle=ISRN%20machine%20vision&rft.au=Lakemond,%20Ruan&rft.date=2013-12-12&rft.volume=2013&rft.spage=1&rft.epage=8&rft.pages=1-8&rft.issn=2090-780X&rft.eissn=2090-780X&rft_id=info:doi/10.1155/2013/261956&rft_dat=%3Ccrossref_hinda%3E10_1155_2013_261956%3C/crossref_hinda%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |