{"id":229,"date":"2022-01-04T17:37:53","date_gmt":"2022-01-04T16:37:53","guid":{"rendered":"http:\/\/lawyer2.mythemecloud.io\/?p=229"},"modified":"2022-01-27T11:25:15","modified_gmt":"2022-01-27T10:25:15","slug":"self-learning-ontology-for-instance-segmentation-of-3d-indoor-point-cloud","status":"publish","type":"post","link":"https:\/\/www.geovast3d.com\/flyvast-wordpress\/2022\/01\/04\/self-learning-ontology-for-instance-segmentation-of-3d-indoor-point-cloud\/","title":{"rendered":"SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"229\" class=\"elementor elementor-229\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8fd09a1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8fd09a1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-e7100aa\" data-id=\"e7100aa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-73bb776 elementor-widget elementor-widget-spacer\" data-id=\"73bb776\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.6.4 - 13-04-2022 *\/\n.e-container.e-container--row .elementor-spacer-inner{width:var(--spacer-size)}.e-container.e-container--column .elementor-spacer-inner,.elementor-column .elementor-spacer-inner{height:var(--spacer-size)}<\/style>\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ed6873b elementor-widget elementor-widget-text-editor\" data-id=\"ed6873b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.6.4 - 13-04-2022 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#818a91;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#818a91;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<div data-draftjs-conductor-fragment=\"{&quot;blocks&quot;:[{&quot;key&quot;:&quot;cnvsm&quot;,&quot;text&quot;:&quot;August 2020&quot;,&quot;type&quot;:&quot;unstyled&quot;,&quot;depth&quot;:0,&quot;inlineStyleRanges&quot;:[],&quot;entityRanges&quot;:[],&quot;data&quot;:{&quot;dynamicStyles&quot;:{&quot;line-height&quot;:&quot;1.3&quot;}}}],&quot;entityMap&quot;:{},&quot;VERSION&quot;:&quot;8.66.8&quot;}\"><span style=\"text-decoration: underline;\">August 2020<\/span><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-top-column elementor-element elementor-element-ee2789f\" data-id=\"ee2789f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-97d7491 elementor-widget elementor-widget-text-editor\" data-id=\"97d7491\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div data-draftjs-conductor-fragment=\"{&quot;blocks&quot;:[{&quot;key&quot;:&quot;b5p3v&quot;,&quot;text&quot;:&quot;Florent Poux&quot;,&quot;type&quot;:&quot;unordered-list-item&quot;,&quot;depth&quot;:0,&quot;inlineStyleRanges&quot;:[{&quot;offset&quot;:0,&quot;length&quot;:12,&quot;style&quot;:&quot;{\\&quot;FG\\&quot;:\\&quot;#2db6d4\\&quot;}&quot;}],&quot;entityRanges&quot;:[{&quot;offset&quot;:0,&quot;length&quot;:12,&quot;key&quot;:0}],&quot;data&quot;:{}},{&quot;key&quot;:&quot;c2nna&quot;,&quot;text&quot;:&quot;Jean-Jacques 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class=\"MIezR Zn7O0 _2zLWO public-DraftStyleDefault-list-ltr rich-content-UL public-DraftStyleDefault-unorderedListItem public-DraftStyleDefault-reset public-DraftStyleDefault-depth0 public-DraftStyleDefault-listLTR\" data-block=\"true\" data-editor=\"editor\" data-offset-key=\"5tdh6-0-0\"><div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"5tdh6-0-0\"><a class=\"_3Bkfb _1lsz7\" href=\"https:\/\/www.researchgate.net\/profile\/Florent-Poux?_sg%5B0%5D=sqSThWCCDsU0Qeijx7msTOMoJuurUkEJtwmq79uSaDHJUK6muvTrjE26TdUdurJajekelN0.5Uzfxm59_gyjp75iOw4RBej5vLGVmWRbYTaQfMClTM7a6wzMPNjtw3A0Uowu5EK8g5wejul3ZhdcRv36BPIl0Q.lHc9xD_7nWjmN6-Cqx-lwqON2_qhr5Aw0G4qjY9DzKPpS-Ln-5XNW15iRvO5CXXc6GWS0nOGO9w0XM2pjX5a3Q&amp;_sg%5B1%5D=XhQoig8fFbROHHCMCDu76FfDjFtb9LE-8q_obL0_haUTTccf20h3AUX0oYNWJxOu2Hz-jDQ.91V6QkzqA5Di-3jer_9Pz6I6EAvhca76FdLptQXNAoG3TByrm5lcMPeliBog_WFYkgVyuA5M-pPrqwH_Ax3tyg\" target=\"_blank\" rel=\"noopener noreferrer\" data-hook=\"linkViewer\"><span data-offset-key=\"5tdh6-0-0\">Florent Poux<\/span><\/a><\/div><\/li><li class=\"MIezR Zn7O0 _2zLWO public-DraftStyleDefault-list-ltr rich-content-UL public-DraftStyleDefault-unorderedListItem public-DraftStyleDefault-depth0 public-DraftStyleDefault-listLTR\" data-block=\"true\" data-editor=\"editor\" data-offset-key=\"e4rff-0-0\"><div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"e4rff-0-0\"><a class=\"_3Bkfb _1lsz7\" href=\"https:\/\/www.researchgate.net\/profile\/Jean-Jacques-Ponciano-2?_sg%5B0%5D=sqSThWCCDsU0Qeijx7msTOMoJuurUkEJtwmq79uSaDHJUK6muvTrjE26TdUdurJajekelN0.5Uzfxm59_gyjp75iOw4RBej5vLGVmWRbYTaQfMClTM7a6wzMPNjtw3A0Uowu5EK8g5wejul3ZhdcRv36BPIl0Q.lHc9xD_7nWjmN6-Cqx-lwqON2_qhr5Aw0G4qjY9DzKPpS-Ln-5XNW15iRvO5CXXc6GWS0nOGO9w0XM2pjX5a3Q&amp;_sg%5B1%5D=XhQoig8fFbROHHCMCDu76FfDjFtb9LE-8q_obL0_haUTTccf20h3AUX0oYNWJxOu2Hz-jDQ.91V6QkzqA5Di-3jer_9Pz6I6EAvhca76FdLptQXNAoG3TByrm5lcMPeliBog_WFYkgVyuA5M-pPrqwH_Ax3tyg\" target=\"_blank\" rel=\"noopener noreferrer\" data-hook=\"linkViewer\"><span data-offset-key=\"e4rff-0-0\">Jean-Jacques Ponciano<\/span><\/a><\/div><\/li><\/ul><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ada1d63 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ada1d63\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0a6ff04\" data-id=\"0a6ff04\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0087238 elementor-widget elementor-widget-spacer\" data-id=\"0087238\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-00dfecb elementor-widget elementor-widget-image\" data-id=\"00dfecb\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.6.4 - 13-04-2022 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/i.imgur.com\/suDu2Eq.png\" title=\"\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8de764c elementor-widget elementor-widget-text-editor\" data-id=\"8de764c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"t m0 xe h8 y50 ff1 fs3 fc0 sc0 ls2 ws5\"><em><span class=\"ff2\">The classification process and naming conventions.<\/span><\/em><\/div><div class=\"t m0 xe h8 y50 ff1 fs3 fc0 sc0 ls2 ws5\"><em><span class=\"ff2\">(a) <\/span>coloured point cloud;<\/em><\/div><div class=\"t m0 xe h8 y50 ff1 fs3 fc0 sc0 ls2 ws5\"><em>(b) segmentation result;<\/em><\/div><div class=\"t m0 xe h8 y50 ff1 fs3 fc0 sc0 ls2 ws5\"><em>(c) semantic\u00a0segmentation;<\/em><\/div><div class=\"t m0 xe h8 y50 ff1 fs3 fc0 sc0 ls2 ws5\"><em>(d) instance segmentation.<\/em><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e673a45 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e673a45\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-16 elementor-top-column elementor-element elementor-element-9435bb3\" data-id=\"9435bb3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-66 elementor-top-column elementor-element elementor-element-c367ae3\" data-id=\"c367ae3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-507cc67 elementor-widget elementor-widget-spacer\" data-id=\"507cc67\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-410574c elementor-widget elementor-widget-heading\" data-id=\"410574c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.6.4 - 13-04-2022 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-large\">Abstract<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d8ece8 elementor-widget elementor-widget-text-editor\" data-id=\"4d8ece8\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div data-draftjs-conductor-fragment=\"{&quot;blocks&quot;:[{&quot;key&quot;:&quot;79j2n&quot;,&quot;text&quot;:&quot;Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation to create relevant clusters constituting the input of the ontology of classification. Guided by semantic descriptions, the object characteristics are modelled in an ontology through OWL2 and SPARQL to permit structural elements classification in an interoperable fashion. The process benefits from a self-learning procedure that improves the object description iteratively in a fully autonomous fashion. Finally, we benchmark the approach against several deep-learning methods on the S3DIS dataset. We highlight full automation, good performances, easy-integration and a precision of 99.99% for planar-dominant classes outperforming state-of-the-art deep learning.&quot;,&quot;type&quot;:&quot;unstyled&quot;,&quot;depth&quot;:0,&quot;inlineStyleRanges&quot;:[],&quot;entityRanges&quot;:[],&quot;data&quot;:{}}],&quot;entityMap&quot;:{},&quot;VERSION&quot;:&quot;8.66.8&quot;}\">Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation to create relevant clusters constituting the input of the ontology of classification. Guided by semantic descriptions, the object characteristics are modelled in an ontology through OWL2 and SPARQL to permit structural elements classification in an interoperable fashion. The process benefits from a self-learning procedure that improves the object description iteratively in a fully autonomous fashion. Finally, we benchmark the approach against several deep-learning methods on the S3DIS dataset. We highlight full automation, good performances, easy-integration and a precision of 99.99% for planar-dominant classes outperforming state-of-the-art deep learning.<\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-996c984 elementor-widget elementor-widget-text-editor\" data-id=\"996c984\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><a href=\"https:\/\/www.researchgate.net\/publication\/343622553_SELF-LEARNING_ONTOLOGY_FOR_INSTANCE_SEGMENTATION_OF_3D_INDOOR_POINT_CLOUD\">En savoir plus&#8230;<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-28acaf0 elementor-widget elementor-widget-spacer\" data-id=\"28acaf0\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-16 elementor-top-column elementor-element elementor-element-9950efc\" data-id=\"9950efc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>August 2020 Florent Poux Jean-Jacques Ponciano The classification process and naming conventions.(a) coloured point cloud;(b) segmentation result;(c) semantic\u00a0segmentation;(d) instance segmentation. Abstract Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12],"tags":[15,14,16],"class_list":["post-229","post","type-post","status-publish","format-standard","hentry","category-articles-scientifiques","tag-3d","tag-intelligence-artificielle","tag-nuage-de-point","entry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD - Flyvast<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.geovast3d.com\/flyvast-wordpress\/2022\/01\/04\/self-learning-ontology-for-instance-segmentation-of-3d-indoor-point-cloud\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD - Flyvast\" \/>\n<meta property=\"og:description\" content=\"August 2020 Florent Poux Jean-Jacques Ponciano The classification process and naming conventions.(a) coloured point cloud;(b) segmentation result;(c) semantic\u00a0segmentation;(d) instance segmentation. Abstract Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. 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