Name: Maximizing Computer Vision's Field of View - Dr. Marc Eder
Start: 2020-09-01T16:00:00+03:00
End: 2020-09-01T17:30:00+03:00

This talk will introduce the emerging field of 360° computer vision, and provide an overview of the spherical distortion problem, highlighting how this distortion affects many of the highest profile problems in computer vision, from deep learning to structure-from-motion and SLAM. It will survey some of the existing work on the topic, and identify 3 guiding principles that drive a general solution to the problem. Finally, we will conclude with some opportunities for further research and some big picture takeaways from work thus far.

The talk is based on CVPR 2020 paper 'Tangent Images for Mitigating Spherical Distortion' - the speaker is the paper's author.

git: https://github.com/meder411/Tangent-Images

Lecture abstract:

The advances in computer vision over the past decade are astounding when you compare to decades prior. If there is one shortcoming to the current engine of progress, it is that its field of view is still largely limited, in the most literal sense. Most vision algorithms are designed with undistorted, central-perspective images in mind. While this constraint is reflective of the prevalence of these types of cameras in circulation, this narrow field of view restricts progress to merely 30° - 60° crops of the world. Yet, not everything can be experienced, augmented, or understood from these small glimpses. This partial view cannot transport someone to another place, nor can it guarantee the context required to augment a scene or assist with a desired task. These applications require capturing a scene in its entirety: in all directions at once. With the advent and growth of commodity 360° cameras, it is now easy to obtain this type of imagery. However, these 360° images suffer from spherical distortion that is mathematically impossible to remove, and which has a powerful, deleterious effect on many algorithms' performance. As a result, it is imperative that we identify ways to reduce the impact of this distortion so that we may expand computer vision's field of view to the full 360°.

Presenter BIO:

Marc Eder recently completed his PhD in computer science at the University of North Carolina at Chapel Hill, where he was advised by Dr-Ing. Jan-Michael Frahm. Marc's research has primarily focused on computer vision problems involving 360° imaging. In particular, he has endeavored to identify new and improved representations for 360° images that can facilitate the seamless application of traditional central-perspective image algorithms. He has also employed this line of work for popular applications such as 3D indoor modeling. Recently, he co-organized the OmniCV Workshop at CVPR 2020, which brought together top computer vision researchers and engineers to discuss their work with omnidirectional images. Marc serves as a reviewer for CVPR, ICCV, and ECCV, among others, and most recently was acknowledged as a top reviewer for ECCV 2020. Before his doctorate in computer vision, Marc received a MS in electrical engineering at Boston University and a BA in history and Islamic & Middle Eastern Studies from Brandeis University. This fall, Marc will be joining Yembo, a San Diego-based startup leveraging computer vision to transform the home-services industry. More information about Marc can be found at www.marceder.com.

This is a technical talk, prior knowledge of deep learning is advised.

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Maximizing Computer Vision's Field of View - Dr. Marc Eder

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