The contribution of this thesis is the development of several new 3D vision algorithms intended for efficient execution on current generation GPUs. All proposed methods address the fully automated creation of dense 2.5D and 3D geometry of objects and environments captured on a sequence of images. The range of depicted methods starts with simple and purely local approaches with very efficient respective implementations. Furthermore, a novel formulation of a semi-global depth estimation approach suitable for fast execution on the GPU is presented. In addition it is shown, that variational methods for depth estimation can benefit significantly from GPU acceleration as well. Finally, highly efficient methods are presented, which generate 3D models from the input image set, either directly from the images or indirectly via intermediate 2.5D geometry. The performance of the developed methods and their respective implementations is evaluated on artificial datasets to obtain quantitative results, and demonstrated in real world applications as well. The proposed methods are incorporated into a complete 3D vision pipeline, which was successfully applied in several research projects.