Densely Sampled Light Field Reconstruction
Modern display systems aim at creating visual content that can be perceived by humans as a natural scene. A number of display prototypes that specifically address the issue of higher realism have emerged in recent years. In order to recreate 3D visual cues, these systems require a high amount of information.
On the scene capture side, various acquisition systems have been developed in recent year, such as plenoptic cameras, arrays of cameras or more complex multi-sensor systems. All these are technology limited and cannot directly capture the high amount of information demanded by emerging immersive displays. Hence, new computational methods are needed to close the gap between the amount of data that can be feasibly sensed, and the visual content required by the new generation of display systems.
The dissertation builds on the concept of plenoptic or light-field function, which plays an important role in quantifying the light coming from a visual scene through the multitude of rays going in any direction, at any intensity and at any instant in time. Such a comprehensive function is multi-dimensional and highly redundant at the same time, which raises the problem of its accurate sampling and reconstruction. A new solution of this problem has been advanced in the present dissertation.
MSc Suren Vagharshakyan’s doctoral thesis, Densely Sampled Light Field Reconstruction, will be publicly examined on Friday 26 June 2020. You can follow the dissertation online, starting at 12 PM. The opponents will be Prof. Sebastian Knorr from Ernst Abbe University of Applied Sciences Jena, Germany and Dr. Martin Alain from Trinity College Dublin, Ireland.
The dissertation is available at http://urn.fi/URN:ISBN:978-952-03-1615-0
Because of the coronavirus, Tampere University’s dissertation defences are not organised as public events. However, it is possible to follow the public examinations via a remote connection. Link to the online event: https://tuni.zoom.us/j/69899498538?pwd=V1JBYjVuUnJCTzVrTWIwdFhHN080Zz09