{"id":11661,"date":"2023-10-01T05:33:48","date_gmt":"2023-10-01T05:33:48","guid":{"rendered":"https:\/\/dsrc.haifa.ac.il\/?p=11661"},"modified":"2023-10-01T07:10:55","modified_gmt":"2023-10-01T07:10:55","slug":"completing-the-picture-in-3d","status":"publish","type":"post","link":"https:\/\/dsrc.haifa.ac.il\/?p=11661","title":{"rendered":"Completing the picture in 3D"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"11661\" class=\"elementor elementor-11661\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-e2c2ef6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e2c2ef6\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\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-d0f2906\" data-id=\"d0f2906\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d56cb5c elementor-invisible elementor-widget elementor-widget-heading\" data-id=\"d56cb5c\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInUp&quot;,&quot;_animation_mobile&quot;:&quot;fadeInUp&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Completing the picture in 3D\n<\/h2>\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<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-d81fe17 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d81fe17\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\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-2ed58e3\" data-id=\"2ed58e3\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3465297 elementor-invisible elementor-widget elementor-widget-heading\" data-id=\"3465297\" data-element_type=\"widget\" data-e-type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInUp&quot;,&quot;_animation_mobile&quot;:&quot;fadeInUp&quot;}\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Azmi Haider# and Dan Rosenbaum -   Department of Computer Science<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-570ebc7 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"570ebc7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\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<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-b88aabd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b88aabd\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\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-b5ca55a\" data-id=\"b5ca55a\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-inner-section elementor-element elementor-element-6128ba2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6128ba2\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\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-33 elementor-inner-column elementor-element elementor-element-9f52bbf\" data-id=\"9f52bbf\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d219a85 elementor-widget elementor-widget-text-editor\" data-id=\"d219a85\" data-element_type=\"widget\" data-e-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\t\t<p><strong>Machine Learning<\/strong><\/p>\t\t\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-inner-column elementor-element elementor-element-3fd24ae\" data-id=\"3fd24ae\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-378761b elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"378761b\" data-element_type=\"widget\" data-e-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\t\t<p><strong>Processing<\/strong><\/p>\t\t\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-inner-column elementor-element elementor-element-6bf714c\" data-id=\"6bf714c\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b05963d elementor-widget__width-initial elementor-widget elementor-widget-text-editor\" data-id=\"b05963d\" data-element_type=\"widget\" data-e-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\t\t<p><strong>PhD Grant 2023<\/strong><\/p>\t\t\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<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-26b1814 elementor-hidden-tablet elementor-hidden-mobile elementor-widget elementor-widget-text-editor\" data-id=\"26b1814\" data-element_type=\"widget\" data-e-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\t\t<p><img decoding=\"async\" class=\"size-medium wp-image-11666 alignleft\" src=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2023\/10\/Azmi-Haider-300x143.png\" alt=\"\" width=\"300\" height=\"143\" srcset=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2023\/10\/Azmi-Haider-300x143.png 300w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2023\/10\/Azmi-Haider.png 307w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p><p>Humans have the ability to imagine a 3D scene from a single image. Given one image of a car, humans can guess how it looks from every angle, Computers do not. This is due to something we call &#8220;prior knowledge&#8221;. The fact that we saw a car before can help us imagine how a new car will look like from a new viewpoint. What we aim to do is to teach computers to develop prior knowledge and use it to generate new images from unseen viewpoints (given one or more images).<\/p><p>Our research suggests creating representations of 3D scenes in a more compact way. The details of a 3D scene are captured in what we term a latent variable. This variable is transformed to and from a complete 3D scene.<\/p><p>A large neural network called a generative model is then trained on the latent variables and is taught to generate new latent variables. In words, this network will understand what constructs a 3D scene through the latent representations. It will learn what these latent representations have in common and what details they have that are transformed into 3D scenes \u2013 what we termed before \u201cprior knowledge\u201d.<\/p><p>Given one image of a scene: We can use it to add information to the latent representation generated (In the generative model). This will force the latent variable generated to have the same information that is in the image in addition to information about 3D structure.<br \/>After completing this process, we can transform the latent variable back to a 3D scene.<\/p><p>In the photo below, you can see a synthetic scene. The image on the left is a ground truth image from a certain angle. The image on the right is the \u201cimagined\u201d image from the model (it is slightly blurrier). You can see clearly that the model was able to capture the 3D structure of the scene and \u201cguess\u201d how the car will look like from a given angle<\/p>\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-12b0e29 elementor-hidden-desktop elementor-widget elementor-widget-text-editor\" data-id=\"12b0e29\" data-element_type=\"widget\" data-e-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\t\t<p><img decoding=\"async\" class=\"size-medium wp-image-11666 alignleft\" src=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2023\/10\/Azmi-Haider-300x143.png\" alt=\"\" width=\"300\" height=\"143\" srcset=\"https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2023\/10\/Azmi-Haider-300x143.png 300w, https:\/\/dsrc.haifa.ac.il\/wp-content\/uploads\/2023\/10\/Azmi-Haider.png 307w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/><\/p><p>\u00a0<\/p><p>\u00a0<\/p><p>\u00a0<\/p><p>\u00a0<\/p><p>Humans have the ability to imagine a 3D scene from a single image. Given one image of a car, humans can guess how it looks from every angle, Computers do not. This is due to something we call &#8220;prior knowledge&#8221;. The fact that we saw a car before can help us imagine how a new car will look like from a new viewpoint. What we aim to do is to teach computers to develop prior knowledge and use it to generate new images from unseen viewpoints (given one or more images).<\/p><p>Our research suggests creating representations of 3D scenes in a more compact way. The details of a 3D scene are captured in what we term a latent variable. This variable is transformed to and from a complete 3D scene.<\/p><p>A large neural network called a generative model is then trained on the latent variables and is taught to generate new latent variables. In words, this network will understand what constructs a 3D scene through the latent representations. It will learn what these latent representations have in common and what details they have that are transformed into 3D scenes \u2013 what we termed before \u201cprior knowledge\u201d.<\/p><p>Given one image of a scene: We can use it to add information to the latent representation generated (In the generative model). This will force the latent variable generated to have the same information that is in the image in addition to information about 3D structure.<br \/>After completing this process, we can transform the latent variable back to a 3D scene.<\/p><p>In the photo below, you can see a synthetic scene. The image on the left is a ground truth image from a certain angle. The image on the right is the \u201cimagined\u201d image from the model (it is slightly blurrier). You can see clearly that the model was able to capture the 3D structure of the scene and \u201cguess\u201d how the car will look like from a given angle<\/p>\t\t\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<\/div>\n\t\t<\/section>\n\t\t\t\t<section data-particle_enable=\"false\" data-particle-mobile-disabled=\"false\" class=\"elementor-section elementor-top-section elementor-element elementor-element-3839ea6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3839ea6\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;jet_parallax_layout_list&quot;:[]}\">\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-4efc12b\" data-id=\"4efc12b\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-152d16e elementor-widget elementor-widget-spacer\" data-id=\"152d16e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\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\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<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Azmi Haider, Dan Rosenbaum<\/p>\n","protected":false},"author":93,"featured_media":11666,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"_acf_changed":false,"_eb_attr":"","footnotes":""},"categories":[88,93,64,79,77,23],"tags":[34],"class_list":["post-11661","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-88","category-computer-science","category-machine-learning","category-phd","category-processing","category-research-blog","tag-machine-learning","entry","has-media"],"acf":[],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - 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