Computational 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.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/671202 |
| Date | 13 September 2021 |
| Creators | Xiong, Jinhui |
| Contributors | Heidrich, Wolfgang, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Ghanem, Bernard, Wonka, Peter, Schindler, Konrad |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
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