{"id":1,"date":"2022-01-04T19:04:49","date_gmt":"2022-01-04T18:04:49","guid":{"rendered":"https:\/\/www.geovast3d.com\/flyvast-wordpress\/?p=1"},"modified":"2022-01-27T11:24:41","modified_gmt":"2022-01-27T10:24:41","slug":"automatic-detection-of-objects-in-3d-point-clouds-based-on-exclusively-semantic-guided-processes","status":"publish","type":"post","link":"https:\/\/www.geovast3d.com\/flyvast-wordpress\/2022\/01\/04\/automatic-detection-of-objects-in-3d-point-clouds-based-on-exclusively-semantic-guided-processes\/","title":{"rendered":"Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1\" class=\"elementor elementor-1\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-14bfb68 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"14bfb68\" 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-b89a114\" data-id=\"b89a114\" 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-a8dba42 elementor-widget elementor-widget-spacer\" data-id=\"a8dba42\" 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-7ba3729 elementor-widget elementor-widget-text-editor\" data-id=\"7ba3729\" 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;f0c0u&quot;,&quot;text&quot;:&quot;October 2019 - International Journal of Geo-Information&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;key&quot;:&quot;18o9p&quot;,&quot;text&quot;:&quot;Project: Knowledge based Object Detection in Images and Point Clouds&quot;,&quot;type&quot;:&quot;unstyled&quot;,&quot;depth&quot;:0,&quot;inlineStyleRanges&quot;:[{&quot;offset&quot;:9,&quot;length&quot;:59,&quot;style&quot;:&quot;UNDERLINE&quot;}],&quot;entityRanges&quot;:[],&quot;data&quot;:{&quot;dynamicStyles&quot;:{&quot;line-height&quot;:&quot;1.3&quot;}}},{&quot;key&quot;:&quot;562tc&quot;,&quot;text&quot;:&quot;&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;}\">\n<div class=\"_25Ehb _3qYRK Zn7O0 public-DraftStyleDefault-block-depth0 public-DraftStyleDefault-text-ltr fixed-tab-size rich_content_line-height-1_3 rich_content_P\" data-block=\"true\" data-editor=\"editor\" data-offset-key=\"foo-0-0\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"foo-0-0\"><span data-offset-key=\"foo-0-0\"><span style=\"text-decoration: underline;\">October 2019<\/span> &#8211; International Journal of Geo-Information<\/span><\/div>\n<\/div>\n<div class=\"_25Ehb _3qYRK Zn7O0 public-DraftStyleDefault-block-depth0 public-DraftStyleDefault-text-ltr fixed-tab-size rich_content_line-height-1_3 rich_content_P\" data-block=\"true\" data-editor=\"editor\" data-offset-key=\"3vha5-0-0\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"3vha5-0-0\"><span data-offset-key=\"3vha5-0-0\"><u>Project<\/u>: <\/span><span data-offset-key=\"3vha5-0-1\">Knowledge based Object Detection in Images and Point Clouds<\/span><\/div>\n<\/div>\n<\/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-8cf8b29\" data-id=\"8cf8b29\" 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-c72ff86 elementor-widget elementor-widget-text-editor\" data-id=\"c72ff86\" 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;eevlm&quot;,&quot;text&quot;:&quot;Jean-Jacques Ponciano&quot;,&quot;type&quot;:&quot;unordered-list-item&quot;,&quot;depth&quot;:0,&quot;inlineStyleRanges&quot;:[{&quot;offset&quot;:0,&quot;length&quot;:21,&quot;style&quot;:&quot;{\\&quot;FG\\&quot;:\\&quot;#2db6d4\\&quot;}&quot;}],&quot;entityRanges&quot;:[{&quot;offset&quot;:0,&quot;length&quot;:21,&quot;key&quot;:0}],&quot;data&quot;:{}},{&quot;key&quot;:&quot;bgkro&quot;,&quot;text&quot;:&quot;Alain Trem\u00e9au&quot;,&quot;type&quot;:&quot;unordered-list-item&quot;,&quot;depth&quot;:0,&quot;inlineStyleRanges&quot;:[{&quot;offset&quot;:0,&quot;length&quot;:13,&quot;style&quot;:&quot;{\\&quot;FG\\&quot;:\\&quot;#2db6d4\\&quot;}&quot;}],&quot;entityRanges&quot;:[{&quot;offset&quot;:0,&quot;length&quot;:13,&quot;key&quot;:1}],&quot;data&quot;:{}},{&quot;key&quot;:&quot;7q640&quot;,&quot;text&quot;:&quot;Frank Boochs&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;:2}],&quot;data&quot;:{}}],&quot;entityMap&quot;:{&quot;0&quot;:{&quot;type&quot;:&quot;LINK&quot;,&quot;mutability&quot;:&quot;MUTABLE&quot;,&quot;data&quot;:{&quot;target&quot;:&quot;_blank&quot;,&quot;rel&quot;:&quot;&quot;,&quot;url&quot;:&quot;https:\/\/www.researchgate.net\/profile\/Jean-Jacques-Ponciano-2?_sg%5B0%5D=xT-16v42028MjFwjipi8DqUzLYyNd5_yQQUHY--KWn7jqq1_SDto6IAeynn0Zx8H2e0Dofc.0ctuDeo83t6FpObB7bx8XgUlxjVHdEHJcSYUMamXh5MlM1IV2ae3DMCM06FqpEKPJqIhhWyp2Tw7SbmrpZwW1Q.zy2O8Y2wP6f3R7VM7cGftef9NpyIJJUGLiMuua1b249dKnNYcVxkojNM86eEXCEBpHDfGdVOE6TEyHO0Ta_raA&amp;_sg%5B1%5D=eg3zHAmksxUdgrfaBvnXE3VRNCV8WnUavk2OtFSVp3819mTmUtNBRo-kuu6icL0km_CDS0k.YV6Fzw6fwQQzaG1a_JFqvSUhkK2CMIRws6ykywzNY1DXUQgXCF02WHNq0_4njfT-p-EV_ia8K3XvhLHjU4T3Jw&quot;}},&quot;1&quot;:{&quot;type&quot;:&quot;LINK&quot;,&quot;mutability&quot;:&quot;MUTABLE&quot;,&quot;data&quot;:{&quot;target&quot;:&quot;_blank&quot;,&quot;rel&quot;:&quot;&quot;,&quot;url&quot;:&quot;https:\/\/www.researchgate.net\/profile\/Alain-Tremeau?_sg%5B0%5D=xT-16v42028MjFwjipi8DqUzLYyNd5_yQQUHY--KWn7jqq1_SDto6IAeynn0Zx8H2e0Dofc.0ctuDeo83t6FpObB7bx8XgUlxjVHdEHJcSYUMamXh5MlM1IV2ae3DMCM06FqpEKPJqIhhWyp2Tw7SbmrpZwW1Q.zy2O8Y2wP6f3R7VM7cGftef9NpyIJJUGLiMuua1b249dKnNYcVxkojNM86eEXCEBpHDfGdVOE6TEyHO0Ta_raA&amp;_sg%5B1%5D=eg3zHAmksxUdgrfaBvnXE3VRNCV8WnUavk2OtFSVp3819mTmUtNBRo-kuu6icL0km_CDS0k.YV6Fzw6fwQQzaG1a_JFqvSUhkK2CMIRws6ykywzNY1DXUQgXCF02WHNq0_4njfT-p-EV_ia8K3XvhLHjU4T3Jw&quot;}},&quot;2&quot;:{&quot;type&quot;:&quot;LINK&quot;,&quot;mutability&quot;:&quot;MUTABLE&quot;,&quot;data&quot;:{&quot;target&quot;:&quot;_blank&quot;,&quot;rel&quot;:&quot;&quot;,&quot;url&quot;:&quot;https:\/\/www.researchgate.net\/profile\/Frank-Boochs?_sg%5B0%5D=xT-16v42028MjFwjipi8DqUzLYyNd5_yQQUHY--KWn7jqq1_SDto6IAeynn0Zx8H2e0Dofc.0ctuDeo83t6FpObB7bx8XgUlxjVHdEHJcSYUMamXh5MlM1IV2ae3DMCM06FqpEKPJqIhhWyp2Tw7SbmrpZwW1Q.zy2O8Y2wP6f3R7VM7cGftef9NpyIJJUGLiMuua1b249dKnNYcVxkojNM86eEXCEBpHDfGdVOE6TEyHO0Ta_raA&amp;_sg%5B1%5D=eg3zHAmksxUdgrfaBvnXE3VRNCV8WnUavk2OtFSVp3819mTmUtNBRo-kuu6icL0km_CDS0k.YV6Fzw6fwQQzaG1a_JFqvSUhkK2CMIRws6ykywzNY1DXUQgXCF02WHNq0_4njfT-p-EV_ia8K3XvhLHjU4T3Jw&quot;}}},&quot;VERSION&quot;:&quot;8.66.8&quot;}\">\n<ul>\n<li 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=\"5dh6i-0-0\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"5dh6i-0-0\"><a class=\"_3Bkfb _1lsz7\" href=\"https:\/\/www.researchgate.net\/profile\/Jean-Jacques-Ponciano-2?_sg%5B0%5D=xT-16v42028MjFwjipi8DqUzLYyNd5_yQQUHY--KWn7jqq1_SDto6IAeynn0Zx8H2e0Dofc.0ctuDeo83t6FpObB7bx8XgUlxjVHdEHJcSYUMamXh5MlM1IV2ae3DMCM06FqpEKPJqIhhWyp2Tw7SbmrpZwW1Q.zy2O8Y2wP6f3R7VM7cGftef9NpyIJJUGLiMuua1b249dKnNYcVxkojNM86eEXCEBpHDfGdVOE6TEyHO0Ta_raA&amp;_sg%5B1%5D=eg3zHAmksxUdgrfaBvnXE3VRNCV8WnUavk2OtFSVp3819mTmUtNBRo-kuu6icL0km_CDS0k.YV6Fzw6fwQQzaG1a_JFqvSUhkK2CMIRws6ykywzNY1DXUQgXCF02WHNq0_4njfT-p-EV_ia8K3XvhLHjU4T3Jw\" target=\"_blank\" rel=\"noopener noreferrer\" data-hook=\"linkViewer\"><span data-offset-key=\"5dh6i-0-0\">Jean-Jacques Ponciano<\/span><\/a><\/div>\n<\/li>\n<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=\"8tqeo-0-0\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"8tqeo-0-0\"><a class=\"_3Bkfb _1lsz7\" href=\"https:\/\/www.researchgate.net\/profile\/Alain-Tremeau?_sg%5B0%5D=xT-16v42028MjFwjipi8DqUzLYyNd5_yQQUHY--KWn7jqq1_SDto6IAeynn0Zx8H2e0Dofc.0ctuDeo83t6FpObB7bx8XgUlxjVHdEHJcSYUMamXh5MlM1IV2ae3DMCM06FqpEKPJqIhhWyp2Tw7SbmrpZwW1Q.zy2O8Y2wP6f3R7VM7cGftef9NpyIJJUGLiMuua1b249dKnNYcVxkojNM86eEXCEBpHDfGdVOE6TEyHO0Ta_raA&amp;_sg%5B1%5D=eg3zHAmksxUdgrfaBvnXE3VRNCV8WnUavk2OtFSVp3819mTmUtNBRo-kuu6icL0km_CDS0k.YV6Fzw6fwQQzaG1a_JFqvSUhkK2CMIRws6ykywzNY1DXUQgXCF02WHNq0_4njfT-p-EV_ia8K3XvhLHjU4T3Jw\" target=\"_blank\" rel=\"noopener noreferrer\" data-hook=\"linkViewer\"><span data-offset-key=\"8tqeo-0-0\">Alain Trem\u00e9au<\/span><\/a><\/div>\n<\/li>\n<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=\"cnelt-0-0\">\n<div class=\"public-DraftStyleDefault-block public-DraftStyleDefault-ltr\" data-offset-key=\"cnelt-0-0\"><a class=\"_3Bkfb _1lsz7\" href=\"https:\/\/www.researchgate.net\/profile\/Frank-Boochs?_sg%5B0%5D=xT-16v42028MjFwjipi8DqUzLYyNd5_yQQUHY--KWn7jqq1_SDto6IAeynn0Zx8H2e0Dofc.0ctuDeo83t6FpObB7bx8XgUlxjVHdEHJcSYUMamXh5MlM1IV2ae3DMCM06FqpEKPJqIhhWyp2Tw7SbmrpZwW1Q.zy2O8Y2wP6f3R7VM7cGftef9NpyIJJUGLiMuua1b249dKnNYcVxkojNM86eEXCEBpHDfGdVOE6TEyHO0Ta_raA&amp;_sg%5B1%5D=eg3zHAmksxUdgrfaBvnXE3VRNCV8WnUavk2OtFSVp3819mTmUtNBRo-kuu6icL0km_CDS0k.YV6Fzw6fwQQzaG1a_JFqvSUhkK2CMIRws6ykywzNY1DXUQgXCF02WHNq0_4njfT-p-EV_ia8K3XvhLHjU4T3Jw\" target=\"_blank\" rel=\"noopener noreferrer\" data-hook=\"linkViewer\"><span data-offset-key=\"cnelt-0-0\">Frank Boochs<\/span><\/a><\/div>\n<\/li>\n<\/ul>\n<\/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-2917db5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2917db5\" 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-2d17a4e\" data-id=\"2d17a4e\" 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-5e01fde elementor-widget elementor-widget-spacer\" data-id=\"5e01fde\" 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-594b22c elementor-widget elementor-widget-image\" data-id=\"594b22c\" 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\/lFnU6dp.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-57849cd elementor-widget elementor-widget-text-editor\" data-id=\"57849cd\" 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 class=\"nova-legacy-e-text nova-legacy-e-text--size-m nova-legacy-e-text--family-sans-serif nova-legacy-e-text--spacing-none nova-legacy-e-text--color-inherit\"><i>System Overview<\/i><\/p>\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-dc3504a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dc3504a\" 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-0e1f3b0\" data-id=\"0e1f3b0\" 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-e8b3026\" data-id=\"e8b3026\" 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-9d383c7 elementor-widget elementor-widget-spacer\" data-id=\"9d383c7\" 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-e6be056 elementor-widget elementor-widget-heading\" data-id=\"e6be056\" 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 and figures<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-16ac693 elementor-widget elementor-widget-text-editor\" data-id=\"16ac693\" 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;dl1oj&quot;,&quot;text&quot;:&quot;In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.&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;}\">In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects.<\/div><div data-draftjs-conductor-fragment=\"{&quot;blocks&quot;:[{&quot;key&quot;:&quot;dl1oj&quot;,&quot;text&quot;:&quot;In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.&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;}\">Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach.<\/div><div data-draftjs-conductor-fragment=\"{&quot;blocks&quot;:[{&quot;key&quot;:&quot;dl1oj&quot;,&quot;text&quot;:&quot;In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.&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;}\">The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility.<\/div><div data-draftjs-conductor-fragment=\"{&quot;blocks&quot;:[{&quot;key&quot;:&quot;dl1oj&quot;,&quot;text&quot;:&quot;In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.&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;}\">The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries.<\/div><div data-draftjs-conductor-fragment=\"{&quot;blocks&quot;:[{&quot;key&quot;:&quot;dl1oj&quot;,&quot;text&quot;:&quot;In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either a specific type of data, a context (indoor, outdoor) or a set of objects. Machine learning-based approaches are more flexible but also constrained as they need annotated data sets to train the learning process. That leads to problems when this data is not available through the specialty of the application field, like archaeology, for example. In order to overcome such constraints, we present a fully semantic-guided approach. The role of semantics is to express all relevant knowledge of the representation of the objects inside the data sets and of the algorithms which address this representation. In addition, the approach contains a learning stage since it adapts the processing according to the diversity of the objects and data characteristics. The semantic is expressed via an ontological model and uses standard web technology like SPARQL queries, providing great flexibility. The ontological model describes the object, the data and the algorithms. It allows the selection and execution of algorithms adapted to the data and objects dynamically. Similarly, processing results are dynamically classified and allow for enriching the ontological model using SPARQL construct queries. The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.&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;}\">The semantic formulated through SPARQL also acts as a bridge between the knowledge contained within the ontological model and the processing branch, which executes algorithms. It provides the capability to adapt the sequence of algorithms to an individual state of the processing chain and makes the solution robust and flexible. The comparison of this approach with others on the same use case shows the efficiency and improvement this approach brings.<\/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-363fd08 elementor-widget elementor-widget-text-editor\" data-id=\"363fd08\" 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\/336389320_Automatic_Detection_of_Objects_in_3D_Point_Clouds_Based_on_Exclusively_Semantic_Guided_Processes\">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-8003e09 elementor-widget elementor-widget-spacer\" data-id=\"8003e09\" 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-e927c4c\" data-id=\"e927c4c\" 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>October 2019 &#8211; International Journal of Geo-Information Project: Knowledge based Object Detection in Images and Point Clouds Jean-Jacques Ponciano Alain Trem\u00e9au Frank Boochs System Overview Abstract and figures In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. Model-driven approaches are typically constrained through their focus on either [&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-1","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>Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes - 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\/automatic-detection-of-objects-in-3d-point-clouds-based-on-exclusively-semantic-guided-processes\/\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Automatic Detection of Objects in 3D Point Clouds Based on Exclusively Semantic Guided Processes - Flyvast\" \/>\n<meta property=\"og:description\" content=\"October 2019 &#8211; International Journal of Geo-Information Project: Knowledge based Object Detection in Images and Point Clouds Jean-Jacques Ponciano Alain Trem\u00e9au Frank Boochs System Overview Abstract and figures In the domain of computer vision, object recognition aims at detecting and classifying objects in data sets. 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