{"version":"1.0","provider_name":"Sparse Image and dual IMU localization for AR glasses","provider_url":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14","title":"Spring '23 results - Sparse Image and dual IMU localization for AR glasses","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"HRmUpL2qsQ\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/12\/17\/spring-23-results\/\">Spring &#8217;23 results<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/2023\/12\/17\/spring-23-results\/embed\/#?secret=HRmUpL2qsQ\" width=\"600\" height=\"338\" title=\"&#8220;Spring &#8217;23 results&#8221; &#8212; Sparse Image and dual IMU localization for AR glasses\" data-secret=\"HRmUpL2qsQ\" 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\/f23team14\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Visual Odometry The inceptionNet-based model produces similar results to the ResNet-based model with nearly half the number of FLOPs. Thus, we went ahead with InceptionNet architecture itself to train on the Smith Hall dataset collected using Aria glasses. The metrics used here are RMSE (Root Mean Square Error) over position and orientation error in terms &hellip; Continue reading \"\"","thumbnail_url":"https:\/\/mscvprojects.ri.cmu.edu\/f23team14\/wp-content\/uploads\/sites\/91\/2023\/05\/image-7-1024x554.png"}