One core aspect of immersive visualization labs is to develop and provide powerful tools and applications that allow for efficient analysis and exploration of scientific data. As the requirements for such applications are often diverse and complex, the same applies to the development process. This has led to a myriad of different tools, frameworks, and approaches that grew and developed over time. The steady advance of commercial off-the-shelf game engines such as Unreal Engine has made them a valuable option for development in immersive visualization labs. In this work, we share our experience of migrating to Unreal Engine as a primary developing environment for immersive visualization applications. We share our considerations on requirements, present use cases developed in our lab to communicate advantages and challenges experienced, discuss implications on our research and development environments, and aim to provide guidance for others within our community facing similar challenges.

Object selection in virtual environments is one of the most common and recurring interaction tasks. Therefore, the used technique can critically influence a system's overall efficiency and usability. IntenSelect is a scoring-based selection-by-volume technique that was shown to offer improved selection performance over conventional raycasting in virtual reality. This initial method, however, is most pronounced for small spherical objects that converge to a point-like appearance only, is challenging to parameterize, and has inherent limitations in terms of flexibility. We present an enhanced version of IntenSelect called IntenSelect+ designed to overcome multiple shortcomings of the original IntenSelect approach. In an empirical within-subjects user study with 42 participants, we compared IntenSelect+ to IntenSelect and conventional raycasting on various complex object configurations motivated by prior work. In addition to replicating the previously shown benefits of IntenSelect over raycasting, our results demonstrate significant advantages of IntenSelect+ over IntenSelect regarding selection performance, task load, and user experience. We, therefore, conclude that IntenSelect+ is a promising enhancement of the original approach that enables faster, more precise, and more comfortable object selection in immersive virtual environments.

In-situ, in-transit, and hybrid approaches have become well-established visualization methods over the last decades. Especially for large simulations, these paradigms enable visualization and additionally allow for early insights. While there has been a lot of research on combining these approaches with classical visualization software, only a few worked on combining in-situ/in-transit approaches with modern game engines. In this paper, we present and demonstrate InsitUE, a Catalyst2 compatible hybrid workflow that enables interactive real-time visualization of simulation results using Unreal Engine.

Due to the ever-increasing availability of high-performance computing infrastructure, developers can simulate increasingly complex models. However, the increased complexity comes with new challenges regarding data processing and visualization due to the sheer size of simulations. Exploring simulation results needs to be handled efficiently via in-situ/in-transit analysis during run-time. However, most existing in-transit solutions require sophisticated and prior knowledge and significant alteration to existing simulation and visualization code, which produces a high entry barrier for many projects. In this work, we report how Insite, a lightweight in-transit pipeline, provided in-transit visualization and computation capability to various research applications in the neuronal network simulation domain. We describe the development process, including feedback from developers and domain experts, and discuss implications.

Neuronal network simulators are central to computational neuroscience, enabling the study of the nervous system through in-silico experiments. Through the utilization of high-performance computing resources, these simulators are capable of simulating increasingly complex and large networks of neurons today. Yet, the increased capabilities introduce a challenge to the analysis and visualization of the simulation results. In this work, we propose a pipeline for in-transit analysis and visualization of data produced by neuronal network simulators. The pipeline is able to couple with simulators, enabling querying, filtering, and merging data from multiple simulation instances. Additionally, the architecture allows user-defined plugins that perform analysis tasks in the pipeline. The pipeline applies traditional REST API paradigms and utilizes data formats such as JSON to provide easy access to the generated data for visualization and further processing. We present and assess the proposed architecture in the context of neuronal network simulations generated by the NEST simulator.

COTS VR hardware and modern game engines create the impression that bringing even complex data into VR has become easy. In this work, we investigate to what extent game engines can support the development of immersive visualization software with a case study. We discuss how the engine can support the development and where it falls short, e.g., failing to provide acceptable rendering performance for medium and large-sized data sets without using more sophisticated features.

We present a modern C++20 interface for MPI 4.0. The interface utilizes recent language features to ease development of MPI applications. An aggregate reflection system enables generation of MPI data types from user-defined classes automatically. Immediate and persistent operations are mapped to futures, which can be chained to describe sequential asynchronous operations and task graphs in a concise way. This work introduces the prominent features of the interface with examples. We further measure its performance overhead with respect to the raw C interface.

Geodesic ray tracing is the numerical method to compute the motion of matter and radiation in spacetime. It enables visualization of the geometry of spacetime and is an important tool to study the gravitational fields in the presence of astrophysical phenomena such as black holes. Although the method is largely established, solving the geodesic equation remains a computa-tionally demanding task. In this work, we present Astray; a high-performance geodesic ray tracing library capable of running on a single or a cluster of computers equipped with compute or graphics processing units. The library is able to visualize any spacetime given its metric tensor and contains optimized implementations of a wide range of spacetimes, including commonly studied ones such as Schwarzschild and Kerr. The performance of the library is evaluated on standard consumer hardware as well as a compute cluster through strong and weak scaling benchmarks. The results indicate that the system is capable of reaching interactive frame rates with increasing use of high-performance computing resources. We further introduce a user interface capable of remote rendering on a cluster for interactive visualization of spacetimes.

Since the beginning of the design and implementation of virtual environments, these systems have been built to give the users the best possible experience. One detrimental factor for the user experience was shown to be a high end-to-end latency, here measured as motion-to-photon latency, of the system. Thus, a lot of research in the past was focused on the measurement and minimization of this latency in virtual environments.Most existing measurement-techniques require either expensive measurement hardware like an oscilloscope, mechanical components like a pendulum or depend on manual evaluation of samples. This paper proposes a concept of an easy to build, low-cost device consisting of a microcontroller, servo motor and a photo diode to measure the motion-to-photon latency in virtual reality environments fully automatically. It is placed or attached to the system, calibrates itself and is controlled/monitored via a web interface. While the general concept is applicable to a variety of VR technologies, this paper focuses on the context of CAVE-like systems.

In this work we evaluate the impact of passive haptic feedback on touch-based menus, given the constraints and possibilities of a seated, desk-based scenario in VR. Therefore, we compare a menu that once is placed on the surface of a desk and once mid-air on a surface in front of the user. The study design is completed by two conditions without passive haptic feedback. In the conducted user study (n = 33) we found effects of passive haptics (present vs- non-present) and menu alignment (desk vs. mid-air) on the task performance and subjective look & feel, however the race between the conditions was close. An overall winner was the mid-air menu with passive haptic feedback, which however raises hardware requirements.

In this work, we evaluate the impact of passive haptic feedback on touch-based menus, given the constraints and possibilities of a seated, desk-based scenario in VR. Therefore, we compare a menu that once is placed on the surface of a desk and once mid-air on a surface in front of the user. The study design is completed by two conditions without passive haptic feedback. In the conducted user study (n=33), we found effects of passive haptics (present vs-non-present) and menu alignment (desk vs. mid-air) on the task performance and subjective look & feel. However, the race between the conditions was close. An overall winner was the mid-air menu with passive haptic feedback, which however raises hardware requirements.