Clouds in the cloud

Dmitry Veikherman, Amit Aides, Yoav Y. Schechner, Aviad Levis

Research output: Contribution to journalConference articlepeer-review

Abstract

Light-field imaging can be scaled up to a very large area, to map the Earth’s atmosphere in 3D. Multiview spaceborne instruments suffer low spatio-temporal-angular resolution, and are very expensive and unscalable. We develop sky light-field imaging, by a wide, scalable network of wide-angle cameras looking upwards, which upload their data to the cloud. This new type of imaging-system poses 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 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.

Original languageEnglish
Pages (from-to)659-674
Number of pages16
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9006
DOIs
StatePublished - 2015
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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