{"version":"1.0","provider_name":"Learning 3D-VLAs with Noisy Miscalibrated Data","provider_url":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8","title":"Related Works: Foundations of 3D Grounding - Learning 3D-VLAs with Noisy Miscalibrated Data","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"KI1fjfQrNK\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/related-works\/\">Related Works: Foundations of 3D Grounding<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/related-works\/embed\/#?secret=KI1fjfQrNK\" width=\"600\" height=\"338\" title=\"&#8220;Related Works: Foundations of 3D Grounding&#8221; &#8212; Learning 3D-VLAs with Noisy Miscalibrated Data\" data-secret=\"KI1fjfQrNK\" 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\/2026teamf8\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Rotary Positional Embeddings (RoPE) This project builds on RoPE, originally a 1D technique for language models to encode relative position through complex rotations. We utilize 3D axial RoPE, which divides feature vectors into chunks (x, y, and z) to capture spatial relationships in three-dimensional space. However, standard RoPE assumes all modalities share a single, perfectly [&hellip;]","thumbnail_url":"https:\/\/mscvprojects.ri.cmu.edu\/2026teamf8\/wp-content\/uploads\/sites\/150\/2026\/05\/Screenshot-2026-05-07-at-8.54.31-PM.png","thumbnail_width":1460,"thumbnail_height":816}