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dc.contributor.advisorBEKAERT, Philippe-
dc.contributor.advisorVAN REETH, Frank-
dc.contributor.authorHERMANS, Chris-
dc.description.abstractIn this dissertation we introduce a new set of 3D shape acquisition methods, which we have called mobile structured light. Unlike the more classical structured light methods, in which a static projector illuminates a (static) scene with a variety of time-varying illumination patterns, our proposed technique makes use of a mobile projector emitting a single static sinusoidal illumination pattern. This projector, which we refer to as a sliding projector, is translated at a constant velocity in the direction of the projector’s horizontal axis. Optionally, we use a secondary illumination pattern, a De Bruin pattern tailored onto the original wave pattern, to allow for online extrinsic calibration of our setup. Illuminating the object in this manner allows us to perform fast per pixel computations, in which we can analyze the incoming illumination using traditional methods from signal processing literature. By employing Fourier analysis to decompose the observed illumination sequence into a corresponding set of frequency components, we are able to recover pixel depth. We have observed that there exist a linear relationship between the depth of a scene point, defined as the distance between the projector’s principal plane and the scene point, and the frequency of the observed illumination sequence in the corresponding pixel. Thus, either with precise precalibration of the setup, or using a secondary pattern for extrinsic post-calibration, we are able to convert these depth values into a valid 3D reconstruction. As we have explicitly cast depth estimation as a signal processing problem, we are able to borrow from the vast literature that exists on the discussed topics. In this dissertation, we have examined the influence of applying frequency refinement techniques from the single tone estimation problem to our own approach, and noticed a significant improvement in accuracy and reduction in required frames. Furthermore, we have shown compatibility between new developments in the area of compressed sensing, which could potentially be applied to a pre-calibrated implementation of our technique.The proposed approach has several advantages over classical structured light methods. As the method performs depth estimation on a per pixel basis, it is able to preserve sharp edges in the produced depth image. Furthermore, unlike classical structured light methods, the quality of our results is not limited by projector or camera resolution, but is solely dependent on the temporal sampling density of the captured image sequence. Additional benefits include a significant robustness against common problems encountered with structured light methods, such as occlusions, specular reflections, subsurface scattering, interreflections, and to a certain extent projector defocus. Finally, we have discussed the dual relationship between epipolar plane image analysis, a passive shape acquisition technique which uses a linearly translating camera in order to capture the scene structure, and the proposed mobile structured light method. EPI analysis exploits the large amount of inherent structure in the recorded image volume to recover scene structure, relying on data redundancy and a color consensus matching criterium to establish cross-frame correspondences. It is this same data redundancy that determines the accuracy of the observed frequency in our proposed dual technique, without the requirements of an order constraint or the restrictive nature of a color consistency assumption. Thus, mobile structured light allows for the acquisition of a much larger variety of materials, whereas EPI analysis was restricted mainly to Lambertertian surfaces.-
dc.titleMobile Structured Light: Reconstruction using a Signal Processing Approach-
dc.typeTheses and Dissertations-
local.type.specifiedPhd thesis-
item.contributorHERMANS, Chris-
item.fulltextWith Fulltext-
item.fullcitationHERMANS, Chris (2011) Mobile Structured Light: Reconstruction using a Signal Processing Approach.-
item.accessRightsOpen Access-
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