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dc.contributor.advisorHeidrich, Wolfgang
dc.contributor.authorXiong, Jinhui
dc.date.accessioned2021-09-14T06:26:10Z
dc.date.available2021-09-14T06:26:10Z
dc.date.issued2021-09-13
dc.identifier.citationXiong, J. (2021). Computational Imaging and Its Applications in Fluids. KAUST Research Repository. https://doi.org/10.25781/KAUST-2R855
dc.identifier.doi10.25781/KAUST-2R855
dc.identifier.urihttp://hdl.handle.net/10754/671202
dc.description.abstractComputational imaging di↵ers from traditional imaging system by integrating an encoded measurement system and a tailored computational algorithm to extract interesting scene features. This dissertation demonstrates two approaches which apply computational imaging methods to the fluid domain. In the first approach, we study the problem of reconstructing time-varying 3D- 3C fluid velocity vector fields. We extend 2D Particle Imaging Velocimetry to three dimensions by encoding depth into color (a “rainbow”). For reconstruction, we derive an image formation model for recovering stationary 3D particle positions. 3D velocity estimation is achieved with a variant of 3D optical flow that accounts for both physical constraints as well as the rainbow image formation model. This velocity field can be used to refine the position estimate by adding physical priors that tie together all the time steps, forming a joint reconstruction scheme. In the second approach, we study the problem of reconstructing the 3D shape of underwater environments. The distortions from the moving water surface provide a changing parallax for each point on the underwater surface. We utilize this observation by jointly estimating both the underwater geometry and the dynamic shape of the water surface. To this end, we propose a novel di↵erentiable framework to tie together all parameters in an integrated image formation model. To our knowledge, this is the first solution that is capable to simultaneously retrieve the structure of dynamic water surfaces and static underwater scene geometry in the wild.
dc.language.isoen
dc.subjectComputational Imaging
dc.subject3D reconstruction
dc.subjectOptimization
dc.titleComputational Imaging and Its Applications in Fluids
dc.typeDissertation
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
thesis.degree.grantorKing Abdullah University of Science and Technology
dc.contributor.committeememberGhanem, Bernard
dc.contributor.committeememberWonka, Peter
dc.contributor.committeememberSchindler, Konrad
thesis.degree.disciplineComputer Science
thesis.degree.nameDoctor of Philosophy
refterms.dateFOA2021-09-14T06:26:11Z
kaust.request.doiyes


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