From 2D to 3D: 4 Ways to Make a 3D Reconstruction from Imagery
03/16/23 • 54 min
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
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
Previous Episode
From Concept to Reality: The Journey of Building Scaniverse
Join our guest, Keith Ito, founder of Scaniverse as we discuss the challenges of creating a 3D capture app for iPhones. Keith goes into depth on balancing speed with quality of 3D output and how he designed an intuitive user experience for his users.
In this episode, we discuss...
- 01:00 - Keith's Ito's background at Google
- 09:44 - What is the Scaniverse app
- 11:43 - What inspired Keith to build Scaniverse
- 17:37 - The challenges of using LiDAR in the early versions of Scaniverse
- 25:54 - How to build a good user experience for 3D capture apps
- 32:00 - The challenges of running photogrammetry on an iPhone
- 37:07 - The future of 3D capture
- 40:57 - Scaniverse's role at Niantic
Learn more about Scaniverse at: https://scaniverse.com/
Follow Keith Ito on Twitter at: https://twitter.com/keeeto
Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly
Follow Jonathan Stephens on Twitter: https://twitter.com/jonstephens85
Follow Jonathan Stephens on LinkedIn: https://www.linkedin.com/in/jonathanstephens/
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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
Next Episode
Understanding Implicit Neural Representations with Itzik Ben-Shabat
In this episode of Computer Vision Decoded, we are going to dive into implicit neural representations.
We are joined by Itzik Ben-Shabat, a Visiting Research Fellow at the Australian National Universit (ANU) and Technion – Israel Institute of Technology as well as the host of the Talking Paper Podcast.
You will learn a core understanding of implicit neural representations, key concepts and terminology, how it's being used in applications today, and Itzik's research into improving output with limit input data.
Episode timeline:
00:00 Intro
01:23 Overview of what implicit neural representations are
04:08 How INR compares and contrasts with a NeRF
08:17 Why did Itzik pursued this line of research
10:56 What is normalization and what are normals
13:13 Past research people should read to learn about the basics of INR
16:10 What is an implicit representation (without the neural network)
24:27 What is DiGS and what problem with INR does it solve?
35:54 What is OG-I NR and what problem with INR does it solve?
40:43 What software can researchers use to understand INR?
49:15 What information should non-scientists be focused to learn about INR?
Itzik's Website: https://www.itzikbs.com/
Follow Itzik on Twitter: https://twitter.com/sitzikbs
Follow Itzik on LinkedIn: https://www.linkedin.com/in/yizhak-itzik-ben-shabat-67b3b1b7/
Talking Papers Podcast: https://talking.papers.podcast.itzikbs.com/
Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly
Follow Jonathan Stephens on Twitter at: https://twitter.com/jonstephens85
Referenced past episode- What is CVPR: https://share.transistor.fm/s/15edb19d
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
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