{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Computer Vision Decoded","title":"From 2D to 3D: 4 Ways to Make a 3D Reconstruction from Imagery","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/998edfb7\"></iframe>","width":"100%","height":180,"duration":3269,"description":"In this episode of Computer Vision Decoded, we are going to dive into 4 different ways to 3D reconstruct a scene with images. Our cohost Jared Heinly, a PhD in the computer science specializing in 3D reconstruction from images, will dive into the 4 distinct strategies and discuss the pros and cons of each.\nLinks to content shared in this episode:\nLive SLAM to measure a stockpile with SR Measure: https://srmeasure.com/professional\nJared's notes on the iPhone LiDAR and SLAM: https://everypoint.medium.com/everypoint-gets-hands-on-with-apples-new-lidar-sensor-44eeb38db579\nHow to capture images for 3D reconstruction: https://youtu.be/AQfRdr_gZ8g\n00:00 Intro\n01:30 3D Reconstruction from Video\n13:48 3D Reconstruction from Images\n28:05 3D Reconstruction from Stereo Pairs\n38:43 3D Reconstruction from SLAM\nFollow Jared Heinly \nTwitter: https://twitter.com/JaredHeinly\nLinkedIn https://www.linkedin.com/in/jheinly/\nFollow Jonathan Stephens\nTwitter: https://twitter.com/jonstephens85\nLinkedIn: https://www.linkedin.com/in/jonathanstephens/\nThis episode is brought to you by EveryPoint. Learn more about how EveryPoint is building an infinitely scalable data collection and processing platform for the next generation of spatial computing applications and services: https://www.everypoint.io","thumbnail_url":"https://img.transistorcdn.com/svbWV6r49-pdpieSXBZA2AID_LcN1G2vaCDgYbQPMcg/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzMxOTQ2LzE2NTU4/MzEwMTItYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}