In recent years, the demand for interactive photorealistic three-dimensional (3D) environments has increased in various fields, including architecture, engineering, and entertainment. However, achieving a balance between the quality and efficiency of high-performance 3D applications and virtual reality (VR) remains challenging. This study addresses this issue by revisiting and extending view interpolation for image-based rendering (IBR), which enables the exploration of spacious open environments in 3D and VR. Therefore, we introduce multimorphing, a novel rendering method based on the spatial data structure of 2D image patches, called the image graph. Using this approach, novel views can be rendered with up to six degrees of freedom using only a sparse set of views. The rendering process does not require 3D reconstruction of the geometry or per-pixel depth information, and all relevant data for the output are extracted from the local morphing cells of the image graph. The detection of parallax image regions during preprocessing reduces rendering artifacts by extrapolating image patches from adjacent cells in real-time. In addition, a GPU-based solution was presented to resolve exposure inconsistencies within a dataset, enabling seamless transitions of brightness when moving between areas with varying light intensities. Experiments on multiple real-world and synthetic scenes demonstrate that the presented method achieves high "VR-compatible" frame rates, even on mid-range and legacy hardware, respectively. While achieving adequate visual quality even for sparse datasets, it outperforms other IBR and current neural rendering approaches. Using the correspondence-based decomposition of input images into morphing cells of 2D image patches, multidimensional image morphing provides high-performance novel view generation, supporting open 3D and VR environments. Nevertheless, the handling of morphing artifacts in the parallax image regions remains a topic for future research.

Superpixels play a crucial role in image processing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent image processing tasks, offering computational advantages over the manipulation of individual pixels. While numerous oversegmentation techniques have emerged in recent years, many rely on predefined initialization and termination criteria. In this paper, a novel top-down superpixel segmentation algorithm called Hierarchical Histogram Threshold Segmentation (HHTS) is introduced. It eliminates the need for initialization and implements auto-termination, outperforming state-of-the-art methods w.r.t. boundary recall. This is achieved by iteratively partitioning individual pixel segments into foreground and background and applying intensity thresholding across multiple color channels. The underlying iterative process constructs a superpixel hierarchy that adapts to local detail distributions until color information exhaustion. Experimental results demonstrate the superiority of the proposed approach in terms of boundary adherence, while maintaining competitive runtime performance on the BSDS500 and NYUV2 datasets. Further-more, an application of HHTS in refining machine learning-based semantic segmentation masks produced by the Segment Anything Foundation Model (SAM) is presented.

Procedural generation of virtual worlds is an important aspect of game development since decades, typically for increasing replayability or for speeding up the level creation process. However, the utilization of this potential has always been a great challenge due to the difficult controllability of the underlying algorithms or limitations to specific level geometries, like 2D regular structures. In this paper, we present a novel approach for semi-automatic generation of a wide variety of 2D/3D corridor and room systems. The underlying processing pipeline is based on a separation between a user-guided generation of a graph-based abstract level structure and a fully-automatic construction of the corresponding geometry using a pre-modeled component library. The core algorithm is built on the computation of an extended minimal spanning tree, which can be controlled by a set of intuitive vertex and edge parameters. First experimental results have shown that our incremental generation pipeline allows the efficient creation of complex indoor levels, minimizing limitations on level and game designers' creativity.

We introduce a novel span-triangle data structure, based on the span-space representation for isosurfaces. It stores all necessary cell information for dynamic manipulation of the isovalue in an efficient way. We have found that using our data structure in combination with point-based techniques, implemented on graphics hardware, effects in real-time rendering and exploration. Our extraction algorithm utilizes an incremental and progressive update scheme, enabling smooth interaction without significant latency. Moreover, the corresponding visualization pipeline is capable of processing large data sets by utilizing all three levels of memory: disk, system and graphics. We address practical usability in actual medical applications, achieving a new level of interactivity.