From 2D to 3D: 4 Ways to Make a 3D Reconstruction from Imagery

Computer Vision Decoded

Вміст надано Jonathan Stephens. Весь вміст подкастів, включаючи епізоди, графіку та описи подкастів, завантажується та надається безпосередньо компанією Jonathan Stephens або його партнером по платформі подкастів. Якщо ви вважаєте, що хтось використовує ваш захищений авторським правом твір без вашого дозволу, ви можете виконати процедуру, описану тут https://uk.player.fm/legal.

2+ y ago 54:29

MP3•Головна епізоду

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.

Links to content shared in this episode:

Live SLAM to measure a stockpile with SR Measure: https://srmeasure.com/professional

Jared's notes on the iPhone LiDAR and SLAM: https://everypoint.medium.com/everypoint-gets-hands-on-with-apples-new-lidar-sensor-44eeb38db579

How to capture images for 3D reconstruction: https://youtu.be/AQfRdr_gZ8g

00:00 Intro
01:30 3D Reconstruction from Video
13:48 3D Reconstruction from Images
28:05 3D Reconstruction from Stereo Pairs
38:43 3D Reconstruction from SLAM

Follow Jared Heinly
Twitter: https://twitter.com/JaredHeinly
LinkedIn https://www.linkedin.com/in/jheinly/

Follow Jonathan Stephens
Twitter: https://twitter.com/jonstephens85
LinkedIn: https://www.linkedin.com/in/jonathanstephens/

This 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

18 епізодів

#Science #Tech #Jonathan Stephens #Computer Vision #Machine Learning #Apple #Photogrammetry #Spatial Computing