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While 360 degree photography has existed for a century, today’s medium of remapped, interactive panoramic photography has exploded in the last 3-4 years, thanks to the simultaneous development of dSLR cameras, broadband, internet mapping APIs, sufficient processing power for image stitching, and development of the (largely open source) software stack for truly high-quality 360 imagery.
The last few years has likewise seen a large growth in spherical panoramic photography by professionals and amateur photographers. While any camera can be used to create a spherical panorama, the craft of VR photography still remains for now a niche due to the requirement for a fisheye lens to produce a serious volume of
VR images. Still, VR photography is today practiced by thousands of people worldwide. How can this be increased still further?
In 2007 Google released Street View – similar but in essential ways different from 360 Cities’ focus. One real benefit of Street View was that it brought VR photography closer to the mainstream. Millions of people are more aware of the existence and benefit of VR imagery – although we should agree that most of the potential hasn’t been realized, and most applications have yet to be developed.
An American living in Prague for 10 years, Jeffrey Martin has a background in Photography and has been an enthusiastic member of the global Panoramic Photography community for the last few years. Jeffrey founded 360Cities in 2007 with his brother David after creating a similar project for Prague 3 years before that. He enjoys travel, experiments, cooking, and his family.
My research interests are in image processing and machine vision. I am interested in the fundamental problem of how we parse an image (just a 2D grid of numbers!) into a coherent and accurate perception of surfaces in three dimensions. To this end, I’m interested in the low-level image features that provide the information to get started, as well as the higher level algorithms that stitch these features into visible surfaces, perceive shape, reason about occlusion, and perform amodal completion (seeing things behind other things). Apart from general image understanding, example applications are human body pose estimation, tracking, video coding, human-computer interaction, stereo vision, and omnidirectional imaging.