Artwork

Content provided by Jonathan Stephens. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jonathan Stephens or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Player FM - Podcast App
Go offline with the Player FM app!

Exploring Depth Maps in Computer Vision

57:31
 
Share
 

Manage episode 467288948 series 3364101
Content provided by Jonathan Stephens. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jonathan Stephens or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of Computer Vision Decoded, Jonathan Stephens and Jared Heinly explore the concept of depth maps in computer vision. They discuss the basics of depth and depth maps, their applications in smartphones, and the various types of depth maps. The conversation delves into the role of depth maps in photogrammetry and 3D reconstruction, as well as future trends in depth sensing and machine learning. The episode highlights the importance of depth maps in enhancing photography, gaming, and autonomous systems.

Key Takeaways:

  • Depth maps represent how far away objects are from a sensor.
  • Smartphones use depth maps for features like portrait mode.
  • There are multiple types of depth maps, including absolute and relative.
  • Depth maps are essential in photogrammetry for creating 3D models.
  • Machine learning is increasingly used for depth estimation.
  • Depth maps can be generated from various sensors, including LiDAR.
  • The resolution and baseline of cameras affect depth perception.
  • Depth maps are used in gaming for rendering and performance optimization.
  • Sensor fusion combines data from multiple sources for better accuracy.
  • The future of depth sensing will likely involve more machine learning applications.

Episode Chapters
00:00 Introduction to Depth Maps

00:13 Understanding Depth in Computer Vision

06:52 Applications of Depth Maps in Photography

07:53 Types of Depth Maps Created by Smartphones

08:31 Depth Measurement Techniques

16:00 Machine Learning and Depth Estimation

19:18 Absolute vs Relative Depth Maps

23:14 Disparity Maps and Depth Ordering

26:53 Depth Maps in Graphics and Gaming

31:24 Depth Maps in Photogrammetry

34:12 Utilizing Depth Maps in 3D Reconstruction

37:51 Sensor Fusion and SLAM Technologies

41:31 Future Trends in Depth Sensing

46:37 Innovations in Computational Photography

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. Learn more at https://www.everypoint.io

  continue reading

16 episodes

Artwork
iconShare
 
Manage episode 467288948 series 3364101
Content provided by Jonathan Stephens. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Jonathan Stephens or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

In this episode of Computer Vision Decoded, Jonathan Stephens and Jared Heinly explore the concept of depth maps in computer vision. They discuss the basics of depth and depth maps, their applications in smartphones, and the various types of depth maps. The conversation delves into the role of depth maps in photogrammetry and 3D reconstruction, as well as future trends in depth sensing and machine learning. The episode highlights the importance of depth maps in enhancing photography, gaming, and autonomous systems.

Key Takeaways:

  • Depth maps represent how far away objects are from a sensor.
  • Smartphones use depth maps for features like portrait mode.
  • There are multiple types of depth maps, including absolute and relative.
  • Depth maps are essential in photogrammetry for creating 3D models.
  • Machine learning is increasingly used for depth estimation.
  • Depth maps can be generated from various sensors, including LiDAR.
  • The resolution and baseline of cameras affect depth perception.
  • Depth maps are used in gaming for rendering and performance optimization.
  • Sensor fusion combines data from multiple sources for better accuracy.
  • The future of depth sensing will likely involve more machine learning applications.

Episode Chapters
00:00 Introduction to Depth Maps

00:13 Understanding Depth in Computer Vision

06:52 Applications of Depth Maps in Photography

07:53 Types of Depth Maps Created by Smartphones

08:31 Depth Measurement Techniques

16:00 Machine Learning and Depth Estimation

19:18 Absolute vs Relative Depth Maps

23:14 Disparity Maps and Depth Ordering

26:53 Depth Maps in Graphics and Gaming

31:24 Depth Maps in Photogrammetry

34:12 Utilizing Depth Maps in 3D Reconstruction

37:51 Sensor Fusion and SLAM Technologies

41:31 Future Trends in Depth Sensing

46:37 Innovations in Computational Photography

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. Learn more at https://www.everypoint.io

  continue reading

16 episodes

All episodes

×
 
Loading …

Welcome to Player FM!

Player FM is scanning the web for high-quality podcasts for you to enjoy right now. It's the best podcast app and works on Android, iPhone, and the web. Signup to sync subscriptions across devices.

 

Quick Reference Guide

Listen to this show while you explore
Play