M.Sc Thesis

M.Sc StudentVeikherman Danny
SubjectClouds in the cloud
DepartmentDepartment of Electrical and Computer Engineering
Supervisor PROF. Yoav Schechner
Full Thesis textFull thesis text - English Version


Light-field imaging has recently received great attention due to its ability to capture the amount of light flowing in every direction through every point in space. Usually, Light-field imaging is performed on table-top sized objects but it can be scaled up to a very large area, to map the Earth's atmosphere in 3D. Multiview spaceborne instruments suffer from low spatio-temporal-angular resolution, and are very expensive and unscalable. In this work, we develop sky light-field imaging, by a wide, scalable network of wide-angle cameras looking upwards, which uploads its data to {\em the cloud}. This new type of imaging-system poses {\em new computational vision and photography problems}, some of which generalize prior monocular tasks. These include radiometric self-calibration across a network, overcoming flare by a network, and background estimation. On the other hand, network redundancy offers {\em solutions} to these problems, which we derive. Based on such solutions, the light-field network enables unprecedented ways to measure nature. We demonstrate this experimentally by 3D recovery of clouds, in high spatio-temporal resolution. It is achieved by space carving of the volumetric distribution of semi-transparent clouds. Such sensing can complement satellite imagery, be useful to meteorology, make aerosol tomography realizable, and give new, powerful tools to atmospheric and avian wildlife scientists.