{"id":1280,"date":"2013-08-17T14:03:28","date_gmt":"2013-08-17T11:03:28","guid":{"rendered":"http:\/\/saisa.eu\/blogs\/Guidance\/?p=1280"},"modified":"2013-08-17T14:03:28","modified_gmt":"2013-08-17T11:03:28","slug":"visualsfm-tool-for-creating-3d-reconstruction-from-photos","status":"publish","type":"post","link":"https:\/\/saisa.eu\/blogs\/Guidance\/?p=1280","title":{"rendered":"VisualSFM, Tool for creating 3D reconstruction from photos"},"content":{"rendered":"<p><a href=\"http:\/\/homes.cs.washington.edu\/~ccwu\/vsfm\/index.html\">VisualSFM<\/a> is a GUI application for 3D reconstruction using structure from motion (SFM).<\/p>\n<blockquote>\n<p>wikipedia: <a href=\"http:\/\/en.wikipedia.org\/wiki\/Structure_from_motion\">Structure from motion<\/a> (SfM) refers to the process of estimating three-dimensional structures from two-dimensional image sequences which may be coupled with local motion signals.<\/p>\n<\/blockquote>\n<p>Plus<\/p>\n<ul dir=\"ltr\">\n<li>\n<div>no installation, just uncompress<\/div>\n<\/li>\n<\/ul>\n<p>Minus<\/p>\n<ul dir=\"ltr\">\n<li>\n<div>addtional files from dependent packages need to be downloaded separately and copied into same directory<\/div>\n<\/li>\n<\/ul>\n<p><strong>Example<\/strong><\/p>\n<p>30 photos (1600*1200) were taken around the table. The scale ribbon on the table shown 1 cm divisions.<\/p>\n<p><img loading=\"lazy\" alt=\"VisualSFM\" src=\"http:\/\/saisa.eu\/blogs\/Guidance\/wp-content\/uploads\/2013\/08\/visualsfm.png\" width=\"550\" height=\"450\" \/><\/p>\n<p>The SfM menu was used:<\/p>\n<ul dir=\"ltr\">\n<li>\n<div>Compute missing match<\/div>\n<\/li>\n<li>\n<div>Reconstruct parse<\/div>\n<\/li>\n<li>\n<div>Recontruct dense<\/div>\n<ul>\n<li>\n<div>after this 3D point cloud is available as ply file, containing around 130000 points<\/div>\n<\/li>\n<\/ul>\n<\/li>\n<li>\n<div>Run PoissonRecon (optional)<\/div>\n<ul>\n<li>\n<div>instead of creatnig 3D surface in Meshlab, this can be used<\/div>\n<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p><strong>Texturing in <a href=\"http:\/\/saisa.eu\/blogs\/Guidance\/?p=1263\">Meshlab<\/a><\/strong><\/p>\n<p>Note, the 3D meshes shown below have not been manually edited, nor smoothed, the meshes are as calculated by VisualSFM or Meshlab.<\/p>\n<p>The texture via &#8220;Vertex attribute transfer&#8221; is shown below. The method is explained <a href=\"http:\/\/arc-team-open-research.blogspot.fi\/2012\/12\/how-to-make-3d-scan-with-pictures-and.html\">here<\/a>.<\/p>\n<p><img loading=\"lazy\" alt=\"meshlab-from-vsfm-vertex-attribute-transfer2-smallwindow\" src=\"http:\/\/saisa.eu\/blogs\/Guidance\/wp-content\/uploads\/2013\/08\/meshlab-from-vsfm-vertex-attribute-transfer2-smallwindow.png\" width=\"550\" height=\"454\" \/><\/p>\n<p>The texture via &#8220;Parameterization + Texture from registered Rasters&#8221; is shown below. The method is explained <a href=\"http:\/\/www.academia.edu\/3649828\/Generating_a_Photogrammetric_model_using_VisualSFM_and_post-processing_with_Meshlab\">here<\/a>.<\/p>\n<p><img loading=\"lazy\" alt=\"meshlab-from-vsfm-step2-smallwindow\" src=\"http:\/\/saisa.eu\/blogs\/Guidance\/wp-content\/uploads\/2013\/08\/meshlab-from-vsfm-step2-smallwindow.png\" width=\"550\" height=\"382\" \/><\/p>\n<p>Looking the 3D object in the <a href=\"http:\/\/saisa.eu\/blogs\/Guidance\/?p=1244\">Art Of Illusion<\/a>, it shows the surface roughness.<\/p>\n<p><img loading=\"lazy\" alt=\"AOI-dog3\" src=\"http:\/\/saisa.eu\/blogs\/Guidance\/wp-content\/uploads\/2013\/08\/aoi-dog3.png\" width=\"550\" height=\"454\" \/><\/p>\n<p><strong>Links<\/strong><\/p>\n<p>Alternative ways to create 3D objects:<\/p>\n<ul>\n<li><a href=\"http:\/\/saisa.eu\/blogs\/Guidance\/?p=1269\">David Laserscanner with webcam &amp; line laser, Tools for capturing 3D objects<\/a><\/li>\n<li><a href=\"http:\/\/saisa.eu\/blogs\/Guidance\/?p=1251\">SCENECT with Kinect, Tools for capturing house layouts in 3D<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). wikipedia: Structure from motion (SfM) refers to the process of estimating three-dimensional structures from two-dimensional image sequences which may be coupled with local motion signals. Plus no &hellip; <a href=\"https:\/\/saisa.eu\/blogs\/Guidance\/?p=1280\">Continue reading <span class=\"meta-nav\">&rarr;<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[17,74,34,6],"tags":[],"_links":{"self":[{"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=\/wp\/v2\/posts\/1280"}],"collection":[{"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1280"}],"version-history":[{"count":0,"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=\/wp\/v2\/posts\/1280\/revisions"}],"wp:attachment":[{"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1280"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1280"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/saisa.eu\/blogs\/Guidance\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1280"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}