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Abstract:
In this thesis, we present our research on new acquisition methods for
reflectance properties of real-world objects. Specifically, we first
show a method for acquiring spatially varying densities in volumes of
translucent, gaseous material with just a single image. This makes the
method applicable to constantly changing phenomena like smoke without
the use of high-speed camera equipment.
Furthermore, we investigated how two well known techniques --
synthetic aperture confocal imaging and algorithmic descattering --
can be combined to help looking through a translucent medium like fog
or murky water. We show that the depth at which we can still see an
object embedded in the scattering medium is increased. In a related
publication, we show how polarization and descattering based on
phase-shifting can be combined for efficient 3D~scanning of translucent
objects. Normally, subsurface scattering hinders the range estimation
by offsetting the peak intensity beneath the surface away from the
point of incidence. With our method, the subsurface scattering is
reduced to a minimum and therefore reliable 3D~scanning is made possible.
Finally, we present a system which recovers surface geometry,
reflectance properties of opaque objects, and prevailing lighting
conditions at the time of image capture from just a small number of
input photographs. While there exist previous approaches to recover
reflectance properties, our system is the first to work on images
taken under almost arbitrary, changing lighting conditions. This
enables us to use images we took from a community photo collection
website.
