{"version":"1.0","provider_name":"Dynamic Octrees for Efficient Volumetric Modeling","provider_url":"https:\/\/mscvprojects.ri.cmu.edu\/2021teamc","title":"Supervised learning approach - Dynamic Octrees for Efficient Volumetric Modeling","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"KI822OiWfG\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2021teamc\/supervised-learning-appraoch\/\">Supervised learning approach<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2021teamc\/supervised-learning-appraoch\/embed\/#?secret=KI822OiWfG\" width=\"600\" height=\"338\" title=\"&#8220;Supervised learning approach&#8221; &#8212; Dynamic Octrees for Efficient Volumetric Modeling\" data-secret=\"KI822OiWfG\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/mscvprojects.ri.cmu.edu\/2021teamc\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"As introduced in the home page, we can now represent an image as a quadtree. But how do we learn the quadtree structure with a CNN?&nbsp; Looking into the literature, a typical approach to generate image from a latent code is to utilize a \u201cdecoder\u201d network. A decoder network consists of a series of transpose&hellip; Continue reading Untitled","thumbnail_url":"https:\/\/lh5.googleusercontent.com\/9vIVQX8DlCaQ7OTJ59oMqHjqbYPbvihO8-cX7Tnbrpj9QLHSJ6nVQWgYr6Sl-7vwD8qHZgOYsd9d65cFuCafgntbk6AhDyu0-sZnFhSRtNbko7YD46DSkD_oDv7mLnO8Bj1EMHs8"}