Explore every episode of the podcast Computer Vision Decoded
| Title | Pub. Date | Duration | |
|---|---|---|---|
| Understanding 3D Reconstruction with COLMAP | 03 Apr 2025 | 00:57:02 | |
In this episode, Jonathan Stephens and Jared Heinly delve into the intricacies of COLMAP, a powerful tool for 3D reconstruction from images. They discuss the workflow of COLMAP, including feature extraction, correspondence search, incremental reconstruction, and the importance of camera models. The conversation also covers advanced topics like geometric verification, bundle adjustment, and the newer GLOMAP method, which offers a faster alternative to traditional reconstruction techniques. Listeners are encouraged to experiment with COLMAP and learn through hands-on experience. 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 | |||
| Tips and Tricks for 3D Reconstruction in Different Environments | 04 Mar 2025 | 01:21:23 | |
In this episode, we discuss practical tips and challenges in 3D reconstruction from images, focusing on various environments such as urban, indoor, and outdoor settings. We explore issues like repetitive structures, lighting conditions, and the impact of reflections and shadows on reconstruction quality. The conversation also touches on the importance of camera motion, lens distortion, and the role of machine learning in enhancing reconstruction processes. Listeners gain insights into optimizing their 3D capture techniques for better results. Key Takeaways
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| How to Capture Images for 3D Reconstruction | 24 Sep 2022 | 01:23:29 | |
In this episode of Computer Vision Decoded, we are going to dive into image capture best practices for 3D reconstruction. At the end of this livestream, you will have learned the basics for capturing scenes and objects. We will also provide a downloadable visual guide for reference on your next 3D reconstruction project. Download the official guide here to follow along: https://tinyurl.com/4n2wspkn 00:00 Intro Watch out episode of Computer Vision in the Wild to learn more about capturing images outside and in busy locations: https://youtu.be/FwVBR6KFjPI Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly 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 | |||
| Is The iPhone 14 Camera Any Good? | 08 Sep 2022 | 01:01:34 | |
In this episode of Computer Vision Decoded, we join Jared Heinly and Jonathan Stephens from EveryPoint for their live reaction to the iPhone 14 series announcement. They go in depth into what all the camera specs mean to the average person. We also explain basics of computational photography and how Apple is able to get great photos from a small camera sensor. 00:00 Intro Follow Jared Heinly on Twitter 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 | |||
| 3D Reconstruction in the Wild | 09 Aug 2022 | 01:01:51 | |
In this episode of Computer Vision Decoded, we sit down with Jared Heinly, Chief Scientist at EveryPoint, to discuss 3D reconstruction in the wild. What does “in the wild” mean? This means 3D reconstructing objects and scenes in non-controlled environments where you may have limitations with lighting, access, reflective surfaces, etc. 00:00 Intro Jared Heinly’s Academic Papers and Projects Paper: Correcting the Duplicate Scene Structure In Sparse 3D Reconstruction Follow Jared Heinly on Twitter 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 | |||
| What is the CVPR Conference? | 01 Jul 2022 | 00:29:17 | |
In this episode of Computer Vision Decoded we dive into Jared Heinly's recent trip to the CVPR Conference. We cover: what the conference about, who should attend, what are the emerging trends in computer vision, how machine learning is being used in 3D reconstruction, and what NeRFs are for. 00:00 - Introduction Follow Jared Heinly on Twitter Episode sponsored by: EveryPoint | |||
| What Do the WWDC Announcements Mean for Computer Vision? | 21 Jun 2022 | 00:20:49 | |
In this inaugural episode of Computer Vision Decoded we dive into the recent announcements at WWDC 2022 and find out what they mean for the computer vision community. We talk about what Apple is doing with their new RoomPlan API and how computer vision scientists can leverage it for better experiences. We also cover the enhancements to video and photo capture during an active ARKit Session. 00:00 - Introduction Follow Jared Heinly on Twitter Episode sponsored by: EveryPoint | |||
| Exploring Depth Maps in Computer Vision | 18 Feb 2025 | 00:57:31 | |
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:
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 | |||
| What's New in 2025 for Computer Vision? | 11 Feb 2025 | 00:50:03 | |
After an 18 month hiatus, we are back! In this episode of Computer Vision Decoded, hosts Jonathan Stephens and Jared Heinly discuss the latest advancements in computer vision technology, personal updates, and insights from the industry. They explore topics such as real-time 3D reconstruction, computer vision research, SLAM, event cameras, and the impact of generative AI on robotics. The conversation highlights the importance of merging traditional techniques with modern machine learning approaches to solve real-world problems effectively. Chapters 00:00 Intro & Personal Updates
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| A Computer Vision Scientist Reacts to the iPhone 15 Announcement | 18 Sep 2023 | 00:42:17 | |
In this episode of Computer Vision Decoded, we are going to dive into our in-house computer vision expert's reaction to the iPhone 15 and iPhone 15 Pro announcement. We dive into the camera upgrades, decode what a quad sensor means, and even talk about the importance of depth maps. Episode timeline: 00:00 Intro 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 | |||
| OpenMVG Decoded: Pierre Moulon's 10 Year Journey Building Open-Source Software | 05 May 2023 | 00:55:44 | |
In this episode of Computer Vision Decoded, we are going to dive into Pierre Moulon's 10 years experience building OpenMVG. We also cover the impact of open-source software in the computer vision industry and everything involved in building your own project. There is a lot to learn here! Our episode guest, Pierre Moulon, is a computer vision research scientist and creator of OpenMVG - a library for computer-vision scientists and targeted for the Multiple View Geometry community. The episode follow's Pierre's journey building OpenMVG which he wrote about as an article in his GitHub repository. Explore OpenMVG on GitHub: https://github.com/openMVG/openMVG Episode timeline: 00:00 Intro Contact: 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 | |||
| Understanding Implicit Neural Representations with Itzik Ben-Shabat | 21 Apr 2023 | 00:55:22 | |
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 Itzik's Website: https://www.itzikbs.com/ Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly 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 | |||
| From 2D to 3D: 4 Ways to Make a 3D Reconstruction from Imagery | 16 Mar 2023 | 00:54:29 | |
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 Follow Jared Heinly Follow Jonathan Stephens 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 | |||
| From Concept to Reality: The Journey of Building Scaniverse | 24 Jan 2023 | 00:50:05 | |
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…
Learn more about Scaniverse at: https://scaniverse.com/ Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly ----- 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 | |||
| Will NeRFs Replace Photogrammetry? | 11 Nov 2022 | 00:52:14 | |
In this episode of Computer Vision Decoded, we are going to dive into one of the hottest topics in the industry: Neural Radiance Fields (NeRFs) We are joined by Matt Tancik, a student pursuing a PhD in the computer science and electrical engineering department at UC Berkeley. He has also contributed research to the original NeRF project in 2020 along with several others since then. Last but not least, he is building NeRFStudio - a collaboration friendly studio for NeRFs. In this episode you will learn about what NeRFs are and more importantly what they are not. Matt goes into the challenges of large scale NeRF creation with his experience with Block-NeRF. Follow Matt's work at https://www.matthewtancik.com/ Get started with Nerfstudio here: https://docs.nerf.studio/en/latest/ Block-NeRF details: https://waymo.com/research/block-nerf/ 00:00 Intro Follow Jared Heinly on Twitter: https://twitter.com/JaredHeinly 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 | |||