<p>This thesis investigates how disparity estimation may be used to visualize an object on a 3D-screen. The first part looks into different methods of disparity estimation, and the second part examines different ways to visualize an object from one or several stereo pairs and a disparity map. Input to the system is one or several stereo pairs, and output is a sequence of images of the input scene but from more angles. This sequence of images can be shown on Setred AB's 3D-screen. The system has high real time demands and the goal is to do the disparity estimation and visualization in real time.</p><p>In the first part of the thesis, three different ways to calculate disparity maps are implemented and compared. The three methods are correlation-based, local structure-based and phase-based techniques. The correlation-based methods cannot satisfy the real-time demands due to the large number of 2D-convolutions required per pixel. The local structure-based methods have too much noise and cannot satisfy the quality requirements. Therefore, the best method by far is the phase-based method. This method has been implemented in Matlab and C and comparisons between the different implementations are presented.</p><p>The quality of the disparity maps is satisfying, but the real-time demands cannot yet be fulfilled. The future work is therefore to optimize the C code and move some functions to a GPU, because a GPU can perform calculations in parallel with the CPU. Another reason is that many of the calculations are related to resizing and warping, which are well-suited to implementation on a GPU.</p>

This thesis investigates how disparity estimation may be used to visualize an object on a 3D-screen. The first part looks into different methods of disparity estimation, and the second part examines different ways to visualize an object from one or several stereo pairs and a disparity map. Input to the system is one or several stereo pairs, and output is a sequence of images of the input scene but from more angles. This sequence of images can be shown on Setred AB's 3D-screen. The system has high real time demands and the goal is to do the disparity estimation and visualization in real time. In the first part of the thesis, three different ways to calculate disparity maps are implemented and compared. The three methods are correlation-based, local structure-based and phase-based techniques. The correlation-based methods cannot satisfy the real-time demands due to the large number of 2D-convolutions required per pixel. The local structure-based methods have too much noise and cannot satisfy the quality requirements. Therefore, the best method by far is the phase-based method. This method has been implemented in Matlab and C and comparisons between the different implementations are presented. The quality of the disparity maps is satisfying, but the real-time demands cannot yet be fulfilled. The future work is therefore to optimize the C code and move some functions to a GPU, because a GPU can perform calculations in parallel with the CPU. Another reason is that many of the calculations are related to resizing and warping, which are well-suited to implementation on a GPU.

The main purpose of this work is to investigate the behavior of the recently released by Microsoft company the Kinect sensor, which contains the properties that go beyond ordinary cameras. Normally, in order to create a 3D reconstruction of the scene two cameras are required. Whereas, the Kinect device, due to the properties of the Infrared projector and sensor allows to create the same type of the reconstruction using only one device. However, the depth images, which are generated by the Infrared laser projector and monochrome sensor in Kinect can contain undefined values. Therefore, in addition to other investigations this project contains an idea how to improve the quality of the depth images. However, the base aim of this work is to perform a reconstruction of the scene based on the color images using pair of Kinects which will be compared with the results generated by using depth information from one Kinect. In addition, the report contains the information how to check that all the performed calculations were done correctly. All  the algorithms which were used in the project as well as the achieved results will be described and discussed in the separate chapters in the current report.

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Disertační práce se zabývá metodami snímání a hodnocení kvality 3D obrazů a videí. Po krátkém shrnutí fyziologie prostorového vnímání, obsahuje práce stav poznání v oblastech problému adaptivní paralaxy a konfigurace kamer pro snímání klasického stereopáru. Taktéž shrnuje dnešní možnosti odhadu hloubkové mapy. Zmíněny jsou aktivní i pasivní metody, detailněji je vysvětleno profilometrické skenování. Byly změřeny některé technické parametry dvou technologií současných 3D zobrazovačů, a to polarizačně-oddělujících a využívajících časový multiplex, například přeslechy mezi levým a pravým snímkem. Jádro práce tvoří nová metoda pro vytváření hloubkové mapy při snímání 3D scény, kterážto byla autorem navržena a testována. Inovativnost tohoto přístupu spočívá v chytré kombinaci současných aktivních a pasivních metod snímání hloubky scény, která vtipně využívá výhod obou metod. Nakonec jsou prezentovány výsledky subjektivních testů kvality 3D videa. Největší přínos zde má navržená metrika modelující výsledky subjektivních testů kvality 3D videa.