In recent years, motion capture technology has found extensive applications in movie and video game production. In many cases, the actor is not merely overlaid into a virtual scene, but has to interact with purely virtual 3D content. Commercial motion capture systems are able to provide reasonable real-time visualization of the virtual scene to the director and cameraman (with the help of different virtual camera systems), but unfortunately, not to the actor. Therefore, a proper visualization of virtual scenes for the actor becomes an important issue.In the thesis, the problem of providing a proper visualization of virtual content to the actor in the motion capture studio without confining him/her with additional peripherals like VR glasses, is addressed. The main objective is to develop a projection based CAVE-like system that provides the actor a proper visualization of the virtual content. As the actor moves through the motion capture volume, the images shown on the walls are adapted to the viewer location such that the viewer receives the intended image regardless of the relative pose of the visualization surfaces. The location and orientation of the viewer result in constant recalculation of the projected sampling grid, which causes a non-uniform sampling pattern and drastic changes in sampling rate. Here we observe and compare the ways to overcome the consequent problems in regular-to-irregular resampling and aliasing, and propose a method to objectively evaluate the quality of the geometry compensation.The outcomes of the work are: a procedure for image geometry correction in a CAVE-like system, a simulation model of the CAVE-like system with the proposed geometry correction technique, a regular-to-irregular re-sampling along with adaptive anti-aliasing method, a method for re-sampling and anti-aliasing as a problem of leas-squared curve fitting with spline basis functions, and a method for performance evaluation of the proposed system, implemented in Matlab. The proposed image based rendering approach allows independence from the image source, in contrast to the existing conventional CAVE-like systems. Therefore, it is more universally applicable. The performance evaluation methods allows system testing and analysis.
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