High-Order Signed Distance Transform of Sampled Signals

Signed distance transforms of sampled signals can be constructed better than the traditional exact signed distance transform. Such a transform is termed the high-order signed distance transform and is defined as satisfying three conditions: the Eikonal equation, recovery by a Heaviside function, and...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Besler, Bryce A, Kemp, Tannis D, Forkert, Nils D, Boyd, Steven K
Format: Artikel
Sprache:eng
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title
container_volume
creator Besler, Bryce A
Kemp, Tannis D
Forkert, Nils D
Boyd, Steven K
description Signed distance transforms of sampled signals can be constructed better than the traditional exact signed distance transform. Such a transform is termed the high-order signed distance transform and is defined as satisfying three conditions: the Eikonal equation, recovery by a Heaviside function, and has an order of accuracy greater than unity away from the medial axis. Such a transform is an improvement to the classic notion of an exact signed distance transform because it does not exhibit artifacts of quantization. A large constant, linear time complexity high-order signed distance transform for arbitrary dimensionality sampled signals is developed based on the high order fast sweeping method. The transform is initialized with an exact signed distance transform and quantization corrected through an upwind solver for the boundary value Eikonal equation. The proposed method cannot attain arbitrary order of accuracy and is limited by the initialization method and non-uniqueness of the problem. However, meshed surfaces are visually smoother and do not exhibit artifacts of quantization in local mean and Gaussian curvature.
doi_str_mv 10.48550/arxiv.2110.13354
format Article
fullrecord <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2110_13354</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2110_13354</sourcerecordid><originalsourceid>FETCH-LOGICAL-a674-fc2827538ff796d4ef026fddd166e21826f5bda1ae8bf030927b30350e14cd83</originalsourceid><addsrcrecordid>eNotT0tuwjAU9IZFBRygK3yBgP82SxRoUwmJRdhHL7EfWCIBOVVFb0-SdjWaj0YzhLxztlZOa7aB9Iw_a8EHgUup1RuxRbxcs1PyIdEyXrrg6T7239A1gZ4TdD3eU0vvSEtoH7fBHUNw6xdkhgOE5T_OSflxOOdFdjx9fuW7YwbGqgwb4YTV0iHarfEqIBMGvffcmCC4G4iuPXAIrkYm2VbYWjKpWeCq8U7OyeqvdRpePVJsIf1W44FqOiBfo8o_jQ</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>High-Order Signed Distance Transform of Sampled Signals</title><source>arXiv.org</source><creator>Besler, Bryce A ; Kemp, Tannis D ; Forkert, Nils D ; Boyd, Steven K</creator><creatorcontrib>Besler, Bryce A ; Kemp, Tannis D ; Forkert, Nils D ; Boyd, Steven K</creatorcontrib><description>Signed distance transforms of sampled signals can be constructed better than the traditional exact signed distance transform. Such a transform is termed the high-order signed distance transform and is defined as satisfying three conditions: the Eikonal equation, recovery by a Heaviside function, and has an order of accuracy greater than unity away from the medial axis. Such a transform is an improvement to the classic notion of an exact signed distance transform because it does not exhibit artifacts of quantization. A large constant, linear time complexity high-order signed distance transform for arbitrary dimensionality sampled signals is developed based on the high order fast sweeping method. The transform is initialized with an exact signed distance transform and quantization corrected through an upwind solver for the boundary value Eikonal equation. The proposed method cannot attain arbitrary order of accuracy and is limited by the initialization method and non-uniqueness of the problem. However, meshed surfaces are visually smoother and do not exhibit artifacts of quantization in local mean and Gaussian curvature.</description><identifier>DOI: 10.48550/arxiv.2110.13354</identifier><language>eng</language><creationdate>2021-10</creationdate><rights>http://creativecommons.org/licenses/by-nc-sa/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2110.13354$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2110.13354$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Besler, Bryce A</creatorcontrib><creatorcontrib>Kemp, Tannis D</creatorcontrib><creatorcontrib>Forkert, Nils D</creatorcontrib><creatorcontrib>Boyd, Steven K</creatorcontrib><title>High-Order Signed Distance Transform of Sampled Signals</title><description>Signed distance transforms of sampled signals can be constructed better than the traditional exact signed distance transform. Such a transform is termed the high-order signed distance transform and is defined as satisfying three conditions: the Eikonal equation, recovery by a Heaviside function, and has an order of accuracy greater than unity away from the medial axis. Such a transform is an improvement to the classic notion of an exact signed distance transform because it does not exhibit artifacts of quantization. A large constant, linear time complexity high-order signed distance transform for arbitrary dimensionality sampled signals is developed based on the high order fast sweeping method. The transform is initialized with an exact signed distance transform and quantization corrected through an upwind solver for the boundary value Eikonal equation. The proposed method cannot attain arbitrary order of accuracy and is limited by the initialization method and non-uniqueness of the problem. However, meshed surfaces are visually smoother and do not exhibit artifacts of quantization in local mean and Gaussian curvature.</description><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotT0tuwjAU9IZFBRygK3yBgP82SxRoUwmJRdhHL7EfWCIBOVVFb0-SdjWaj0YzhLxztlZOa7aB9Iw_a8EHgUup1RuxRbxcs1PyIdEyXrrg6T7239A1gZ4TdD3eU0vvSEtoH7fBHUNw6xdkhgOE5T_OSflxOOdFdjx9fuW7YwbGqgwb4YTV0iHarfEqIBMGvffcmCC4G4iuPXAIrkYm2VbYWjKpWeCq8U7OyeqvdRpePVJsIf1W44FqOiBfo8o_jQ</recordid><startdate>20211025</startdate><enddate>20211025</enddate><creator>Besler, Bryce A</creator><creator>Kemp, Tannis D</creator><creator>Forkert, Nils D</creator><creator>Boyd, Steven K</creator><scope>GOX</scope></search><sort><creationdate>20211025</creationdate><title>High-Order Signed Distance Transform of Sampled Signals</title><author>Besler, Bryce A ; Kemp, Tannis D ; Forkert, Nils D ; Boyd, Steven K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-fc2827538ff796d4ef026fddd166e21826f5bda1ae8bf030927b30350e14cd83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Besler, Bryce A</creatorcontrib><creatorcontrib>Kemp, Tannis D</creatorcontrib><creatorcontrib>Forkert, Nils D</creatorcontrib><creatorcontrib>Boyd, Steven K</creatorcontrib><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Besler, Bryce A</au><au>Kemp, Tannis D</au><au>Forkert, Nils D</au><au>Boyd, Steven K</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High-Order Signed Distance Transform of Sampled Signals</atitle><date>2021-10-25</date><risdate>2021</risdate><abstract>Signed distance transforms of sampled signals can be constructed better than the traditional exact signed distance transform. Such a transform is termed the high-order signed distance transform and is defined as satisfying three conditions: the Eikonal equation, recovery by a Heaviside function, and has an order of accuracy greater than unity away from the medial axis. Such a transform is an improvement to the classic notion of an exact signed distance transform because it does not exhibit artifacts of quantization. A large constant, linear time complexity high-order signed distance transform for arbitrary dimensionality sampled signals is developed based on the high order fast sweeping method. The transform is initialized with an exact signed distance transform and quantization corrected through an upwind solver for the boundary value Eikonal equation. The proposed method cannot attain arbitrary order of accuracy and is limited by the initialization method and non-uniqueness of the problem. However, meshed surfaces are visually smoother and do not exhibit artifacts of quantization in local mean and Gaussian curvature.</abstract><doi>10.48550/arxiv.2110.13354</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext_linktorsrc
identifier DOI: 10.48550/arxiv.2110.13354
ispartof
issn
language eng
recordid cdi_arxiv_primary_2110_13354
source arXiv.org
title High-Order Signed Distance Transform of Sampled Signals
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T02%3A11%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=High-Order%20Signed%20Distance%20Transform%20of%20Sampled%20Signals&rft.au=Besler,%20Bryce%20A&rft.date=2021-10-25&rft_id=info:doi/10.48550/arxiv.2110.13354&rft_dat=%3Carxiv_GOX%3E2110_13354%3C/arxiv_GOX%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