Route planning is a common task that often requires additional information on points of interest. Augmented Reality (AR) enables mobile users to explore text labels and provides a composite view associated with additional information in a real-world environment. Displaying all labels for points of interest on a mobile device will lead to unwanted overlaps, and thus a context-responsive strategy to properly arrange labels is expected. This framework should consider removing overlaps,
the correct level-of-detail to be presented, and also label coherence.
This is necessary as the viewing angle in an AR system may changes frequently due to users' behaviors. The consistency of labels plays an essential role in retaining user experience and knowledge, as well as avoiding motion sickness. In this thesis, we aim to develop an approach that systematically manages label visibility and levels-of-detail, as
well as eliminates unexpected incoherent label movement. To achieve this, we introduce three label management strategies, including (1) occlusion management, (2) level-of-detail management, and (3) coherence management. A greedy approach is developed for fast occlusion handling. A level-of-detail scheme is adopted to arrange various types of labels in AR. A 3D scene manipulation is built to simultaneously suppress the incoherent behaviors induced by the changes of viewing angles. Finally, we present our approach's feasibility and applicability by demonstrating one synthetic and two real-world scenarios, followed by a qualitative user study.
Inline Computational Imaging (ICI) is a novel single sensor technology, capable of simultaneous 2D and 3D inline inspection invented by AIT Austrian Institute of Technology GmbH. It combines the advantages of light field imaging and photometric stereo into one compact solution. ICI technology is a new type of image acquisition system, combined with smart algorithms for high resolution and high speed 2D and 3D inspection (standard setup: 20µm/pixel optical resolution and maximum acquisition speed of 500mm/s). The underlying ICI software processes area image data and produces 3D point cloud data.
To render ICI point cloud data in the browser, AIT evaluated Potree as an appropriate solution. Potree streams and renders point cloud data pre-processed with PotreeConverter proposed in “Fast Out-of-Core Octree Generation for Massive Point Clouds”. This software converts point clouds to a hierarchically Level-of-detail (LOD) structure, required for web based consumption.
As the complete ICI 3D-reconstruction pipeline is implemented in CUDA and runs entirely on the GPU, AIT wants to perform the point cloud to octree conversion on the GPU too. This master’s thesis aims to implement the complete octree generation process for Potree on the GPU using CUDA. Beside the performance improvements, this approach enables a direct compatibility with the point cloud results from the ICI pipeline. To minimize Aliasing artifacts during rendering, the octree will be implemented using a replacement scheme instead of an additive scheme.
Long before the onset of computer technology, anatomical sculptures were already used for educational purposes. Digital imaging technology and its incorporation into the clinical workflow through the advancements of medical visualization led to a steady decline in the use of sculpture-based teaching aids. Currently, anatomical volume visualizations are predominantly presented on computer screens. Recent developments in augmented, mixed, and virtual reality offer new, exciting ways to digitally display medical imaging data. In recent years, the application of real-world sculptures to display patient imaging data has seen a resurgence through the field of data physicalization. Predominantly, it has been used to enhance the education of medical personnel and laymen through the use of physical models. Expensive 3D printing technology is often employed in the creation of high fidelity anatomical sculptures, with realistic look-and-feel. However, few approaches make use of affordable physicalizations in the field of layman anatomical education.
In the course of this thesis different ways to introduce self-made, custom physicalizations into layman medical education are explored. We propose a suitable concept, the Vologram, to display medical volume data in a visually appealing way for medical non-experts. This takes the form of slide-based sculptures, made out of affordable materials available to the general public with a high degree of interactivity, and can be produced through commonly available means. To support a customizable workflow in the creation of these sculptures, we provide a stand-alone desktop application, which allows layman users to create custom educational sculptures. Real medical imaging data can be filtered and displayed in different ways, delivering optically diverse results. We evaluate the concept in a small scale study, to determine the effect of interactive medical visualizations as opposed to physicalizations on the target audience. The results of this study point to a great potential for the application of interactive educational concepts for layman anatomical education.
20 + 10
Renata Raidou and Hsiang-Yun Wu
Institute of Visual Computing & Human-Centered Technology
Favoritenstr. 9-11 / E193-02
Austria - Europe