Yerleşim alanlarının görüntülenmesi

Modeling and visualization of large geometric environments is a popular researcharea in computer graphics. In this dissertation, a framework for modeling andstereoscopic visualization of large and complex urban environments is presented.The occlusion culling and view-frustum culling is performed to eliminate mostof the geometry that do not contribute to the user?s final view. For the occlu-sion culling process, the shrinking method is employed but performed using anovel Minkowski-difference-based approach. In order to represent partial visibil-ity, a novel building representation method, called the slice-wise representationis developed. This method is able to represent the preprocessed partial visibilitywith huge reductions in the storage requirement. The resultant visibility list isrendered using a graphics-processing-unit-based algorithm, which perfectly fitsinto the proposed slice-wise representation. The stereoscopic visualization de-pends on the calculated eye positions during walkthrough and the visibility listsfor both eyes are determined using the preprocessed occlusion information. Theview-frustum culling operation is performed once instead of two for both eyes.The proposed algorithms were implemented on personal computers. Performanceexperiments show that, the proposed occlusion culling method and the usage ofthe slice-wise representation increase the frame rate performance by 81 %; thegraphics-processing-unit-based display algorithm increases it by an additional315 % and decrease the storage requirement by 97 % as compared to occlusionculling using building-level granularity and not using the graphics hardware. Weshow that, a smooth and real-time visualization of large and complex urban en-vironments can be achieved by using the proposed framework.Keywords: Stereoscopic visualization, slice-wise representation, space subdivi-sion, octree, occlusion culling, occluder shrinking, Minkowski difference, from-region visibility, urban visualization, visibility processing.iv