Capturing and Reconstructing the Appearance of Complex 3D Scenes
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In this thesis, we present our research on new acquisition methods forreflectance properties of real-world objects. Specifically, we firstshow a method for acquiring spatially varying densities in volumes oftranslucent, gaseous material with just a single image. This makes themethod applicable to constantly changing phenomena like smoke withoutthe 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 fogor murky water. We show that the depth at which we can still see anobject embedded in the scattering medium is increased. In a relatedpublication, we show how polarization and descattering based onphase-shifting can be combined for efficient 3D~scanning oftranslucent objects. Normally, subsurface scattering hinders the rangeestimation by offsetting the peak intensity beneath the surface awayfrom the point of incidence. With our method, the subsurfacescattering is reduced to a minimum and therefore reliable 3D~scanningis made possible.Finally, we present a system which recovers surface geometry,reflectance properties of opaque objects, and prevailing lightingconditions at the time of image capture from just a small number ofinput photographs. While there exist previous approaches to recoverreflectance properties, our system is the first to work on imagestaken under almost arbitrary, changing lighting conditions. Thisenables us to use images we took from a community photo collectionwebsite.