UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality.

Tracking body and hand motions in 3D space is essential for social and self-presence in augmented and virtual environments. Unlike the popular 3D pose estimation setting, the problem is often formulated as egocentric tracking based on embodied perception (e.g., egocentric cameras, handheld sensors). In this article, we propose a new data-driven framework for egocentric body tracking, targeting challenges of omnipresent occlusions in optimization-based methods (e.g., inverse kinematics solvers). We first collect a large-scale motion capture dataset with both body and finger motions using optical markers and inertial sensors. This dataset focuses on social scenarios and captures ground truth poses under self-occlusions and body-hand interactions. We then simulate the occlusion patterns in head-mounted camera views on the captured ground truth using a ray casting algorithm and learn a deep neural network to infer the occluded body parts. Our experiments show that our method is able to generate high-fidelity embodied poses by applying the proposed method to the task of real-time egocentric body tracking, finger motion synthesis, and 3-point inverse kinematics.

On Dynamic Scheduling for the GPU and its Applications in Computer Graphics and Beyond.

During the last decade we have witnessed a severe change in computing, as processor clock-rates stopped increasing. Thus, the arguable only way to increase processing power is switching to a parallel computing architecture, like the graphics processing unit (GPU). While a GPU offers tremendous processing power, harnessing this power is often difficult. In our research we tackle this issue, providing various components to allow a wider class of algorithms to execute efficiently on the GPU. These efforts include new processing models for dynamic algorithms with various degrees of parallelism, a versatile task scheduler, based on highly efficient work queues which also support dynamic priority scheduling, and efficient dynamic memory management. Our scheduling strategies advance the state-of-the-art algorithms in the field of rendering, visualization, and geometric modeling. In the field of rendering, we provide algorithms that can significantly speed-up image generation, assigning more processing power to the most important image regions. In the field of geometric modeling we provide the first GPU-based grammar evaluation system that can generate and render cities in real-time which otherwise take hours to generate and could not fit into GPU memory. Finally, we show that mesh processing algorithms can be computed significantly faster on the GPU when parallelizing them with advanced scheduling strategies.

Forced-Air Warming During Pediatric Surgery: A Randomized Comparison of a Compressible with a Noncompressible Warming System.

BACKGROUND: Perioperative hypothermia is a common problem, challenging the anesthesiologist and influencing patient outcome. Efficient and safe perioperative active warming is therefore paramount; yet, it can be particularly challenging in pediatric patients. Forced-air warming technology is the most widespread patient-warming option, with most forced-air warming systems consisting of a forced-air blower connected to a compressible, double layer plastic and/or a paper blanket with air holes on the patient side. We compared an alternative, forced-air, noncompressible, under-body patient-warming mattress (Baby/Kleinkinddecke of MoeckWarmingSystems, Moeck und Moeck GmbH; group MM) with a standard, compressible warming mattress system (Pediatric Underbody, Bair Hugger, 3M; group BH). METHODS: The study included 80 patients aged <2 years, scheduled for elective surgery. After a preoperative core temperature measurement, the patients were placed on the randomized mattress in the operation theater and 4 temperature probes were applied rectally and to the patients' skin. The warming devices were turned on as soon as possible to the level for pediatric patients as recommended by the manufacturer (MM = 40°C, BH = 43°C). RESULTS: There was a distinct difference of temperature slope between the 2 groups: core temperatures of patients in the group MM remained stable and mean of the core temperature of patients in the group BH increased significantly (difference: +1.48°C/h; 95% confidence interval, 0.82-2.15°C/h; P = 0.0001). The need for temperature downregulation occurred more often in the BH group, with 22 vs 7 incidences (RR, 3.14; 95% confidence interval, 1.52-6.52; P = 0.0006). Skin temperatures were all lower in the MM group. Perioperatively, no side effects related to a warming device were observed in any group. CONCLUSIONS: Both devices are feasible choices for active pediatric patient warming, with the compressible mattress system being better suited to increase core temperature. The use of lower pediatric forced-air temperature settings, as recommended by the manufacturer, in the noncompressible mattress group resulted in more stable core temperature conditions, with fewer forced-air temperature adjustments necessary to avoid hyperthermia.

Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices.

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available.

Show Me the Invisible: Visualizing Hidden Content.

Content on computer screens is often inaccessible to users because it is hidden, e.g., occluded by other windows, outside the viewport, or overlooked. In search tasks, the efficient retrieval of sought content is important. Current software, however, only provides limited support to visualize hidden occurrences and rarely supports search synchronization crossing application boundaries. To remedy this situation, we introduce two novel visualization methods to guide users to hidden content. Our first method generates awareness for occluded or out-of-viewport content using see-through visualization. For content that is either outside the screen's viewport or for data sources not opened at all, our second method shows off-screen indicators and an on-demand smart preview. To reduce the chances of overlooking content, we use visual links, i.e., visible edges, to connect the visible content or the visible representations of the hidden content. We show the validity of our methods in a user study, which demonstrates that our technique enables a faster localization of hidden content compared to traditional search functionality and thereby assists users in information retrieval tasks.

Noise-based volume rendering for the visualization of multivariate volumetric data.

Analysis of multivariate data is of great importance in many scientific disciplines. However, visualization of 3D spatially-fixed multivariate volumetric data is a very challenging task. In this paper we present a method that allows simultaneous real-time visualization of multivariate data. We redistribute the opacity within a voxel to improve the readability of the color defined by a regular transfer function, and to maintain the see-through capabilities of volume rendering. We use predictable procedural noise--random-phase Gabor noise--to generate a high-frequency redistribution pattern and construct an opacity mapping function, which allows to partition the available space among the displayed data attributes. This mapping function is appropriately filtered to avoid aliasing, while maintaining transparent regions. We show the usefulness of our approach on various data sets and with different example applications. Furthermore, we evaluate our method by comparing it to other visualization techniques in a controlled user study. Overall, the results of our study indicate that users are much more accurate in determining exact data values with our novel 3D volume visualization method. Significantly lower error rates for reading data values and high subjective ranking of our method imply that it has a high chance of being adopted for the purpose of visualization of multivariate 3D data.

Context-preserving visual links.

Evaluating, comparing, and interpreting related pieces of information are tasks that are commonly performed during visual data analysis and in many kinds of information-intensive work. Synchronized visual highlighting of related elements is a well-known technique used to assist this task. An alternative approach, which is more invasive but also more expressive is visual linking in which line connections are rendered between related elements. In this work, we present context-preserving visual links as a new method for generating visual links. The method specifically aims to fulfill the following two goals: first, visual links should minimize the occlusion of important information; second, links should visually stand out from surrounding information by minimizing visual interference. We employ an image-based analysis of visual saliency to determine the important regions in the original representation. A consequence of the image-based approach is that our technique is application-independent and can be employed in a large number of visual data analysis scenarios in which the underlying content cannot or should not be altered. We conducted a controlled experiment that indicates that users can find linked elements in complex visualizations more quickly and with greater subjective satisfaction than in complex visualizations in which plain highlighting is used. Context-preserving visual links were perceived as visually more attractive than traditional visual links that do not account for the context information.