6 research outputs found
scenery: Flexible Virtual Reality Visualization on the Java VM
Life science today involves computational analysis of a large amount and
variety of data, such as volumetric data acquired by state-of-the-art
microscopes, or mesh data from analysis of such data or simulations.
Visualization is often the first step in making sense of data, and a crucial
part of building and debugging analysis pipelines. It is therefore important
that visualizations can be quickly prototyped, as well as developed or embedded
into full applications. In order to better judge spatiotemporal relationships,
immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and
associated controllers are becoming invaluable tools. In this work we introduce
scenery, a flexible VR/AR visualization framework for the Java VM that can
handle mesh and large volumetric data, containing multiple views, timepoints,
and color channels. scenery is free and open-source software, works on all
major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce
scenery's main features and example applications, such as its use in VR for
microscopy, in the biomedical image analysis software Fiji, or for visualizing
agent-based simulations.Comment: Added IEEE DOI, version published at VIS 201
Diva: A Declarative and Reactive Language for In-Situ Visualization
The use of adaptive workflow management for in situ visualization and
analysis has been a growing trend in large-scale scientific simulations.
However, coordinating adaptive workflows with traditional procedural
programming languages can be difficult because system flow is determined by
unpredictable scientific phenomena, which often appear in an unknown order and
can evade event handling. This makes the implementation of adaptive workflows
tedious and error-prone. Recently, reactive and declarative programming
paradigms have been recognized as well-suited solutions to similar problems in
other domains. However, there is a dearth of research on adapting these
approaches to in situ visualization and analysis. With this paper, we present a
language design and runtime system for developing adaptive systems through a
declarative and reactive programming paradigm. We illustrate how an adaptive
workflow programming system is implemented using our approach and demonstrate
it with a use case from a combustion simulation.Comment: 11 pages, 5 figures, 6 listings, 1 table, to be published in LDAV
2020. The article has gone through 2 major revisions: Emphasized
contributions, features and examples. Addressed connections between DIVA and
FRP. In sec. 3, we fixed a design flaw and addressed it in sec. 3.3-3.4.
Re-designed sec. 5 with a more concrete example and benchmark results.
Simplified the syntax of DIV
Interactive web-based visualization
The visualization of large amounts of data, which cannot be easily copied for processing on a userâs local machine, is not yet a fully solved problem. Remote visualization represents one possible solution approach to the problem, and has long been an important research topic. Depending on the device used, modern hardware, such as high-performance GPUs, is sometimes not available. This is another reason for the use of remote visualization. Additionally, due to the growing global networking and collaboration among research groups, collaborative remote visualization solutions are becoming more important. The additional use of collaborative visualization solutions is eventually due to the growing global networking and collaboration among research groups.
The attractiveness of web-based remote visualization is greatly increased by the wide availability of web browsers on almost all devices; these are available today on all systems - from desktop computers to smartphones. In order to ensure interactivity, network bandwidth and latency are the biggest challenges that web-based visualization algorithms have to solve. Despite the steady improvements in available bandwidth, these improvements are still significantly slower than, for example, processor performance, resulting in increasing the impact of this bottleneck. For example, visualization of large dynamic data in low-bandwidth environments can be challenging because it requires continuous data transfer. However, bandwidth improvement alone cannot improve the latency because it is also affected by factors such as the distance between server and client and network utilization.
To overcome these challenges, a combination of techniques is needed to customize the individual processing steps of the visualization pipeline, from efficient data representation to hardware-accelerated rendering on the client side. This thesis first deals with related work in the field of remote visualization with a particular focus on interactive web-based visualization and then presents techniques for interactive visualization in the browser using modern web standards such as WebGL and HTML5. These techniques enable the visualization of dynamic molecular data sets with more than one million atoms at interactive frame rates using GPU-based ray casting. Due to the limitations which exist in a browser-based environment, the concrete implementation of the GPU-based ray casting had to be customized. Evaluation of the resulting performance shows that GPU-based techniques enable the interactive rendering of large data sets and achieve higher image quality compared to polygon-based techniques.
In order to reduce data transfer times and network latency, and improve rendering speed, efficient approaches for data representation and transmission are used. Furthermore, this thesis introduces a GPU-based volume-ray marching technique based on WebGL 2.0, which uses progressive brick-wise data transfer, as well as multiple levels of detail in order to achieve interactive volume rendering of datasets stored on a server.
The concepts and results presented in this thesis contribute to the further spread of interactive web-based visualization. The algorithmic and technological advances that have been achieved form a basis for further development of interactive browser-based visualization applications. At the same time, this approach has the potential for enabling future collaborative visualization in the cloud.Die Visualisierung groĂer Datenmengen, welche nicht ohne Weiteres zur Verarbeitung auf den lokalen Rechner des Anwenders kopiert werden können, ist ein bisher nicht zufriedenstellend gelöstes Problem. Remote-Visualisierung stellt einen möglichen Lösungsansatz dar und ist deshalb seit langem ein relevantes Forschungsthema. AbhĂ€ngig vom verwendeten EndgerĂ€t ist moderne Hardware, wie etwa performante GPUs, teilweise nicht verfĂŒgbar. Dies ist ein weiterer Grund fĂŒr den Einsatz von Remote-Visualisierung. Durch die zunehmende globale Vernetzung und Kollaboration von Forschungsgruppen gewinnt kollaborative Remote-Visualisierung zusĂ€tzlich an Bedeutung.
Die AttraktivitĂ€t web-basierter Remote-Visualisierung wird durch die weitreichende VerfĂŒgbarkeit von Web-Browsern auf nahezu allen EndgerĂ€ten enorm gesteigert; diese sind heutzutage auf allen Systemen - vom Desktop-Computer bis zum Smartphone - vorhanden. Bei der GewĂ€hrleistung der InteraktivitĂ€t sind Bandbreite und Latenz der Netzwerkverbindung die gröĂten Herausforderungen, welche von web-basierten Visualisierungs-Algorithmen gelöst werden mĂŒssen. Trotz der stetigen Verbesserungen hinsichtlich der verfĂŒgbaren Bandbreite steigt diese signifikant langsamer als beispielsweise die Prozessorleistung, wodurch sich die Auswirkung dieses Flaschenhalses immer weiter verstĂ€rkt. So kann beispielsweise die Visualisierung groĂer dynamischer Daten in Umgebungen mit geringer Bandbreite eine Herausforderung darstellen, da kontinuierlicher Datentransfer benötigt wird. Dennoch kann die alleinige Verbesserung der Bandbreite keine entsprechende Verbesserung der Latenz bewirken, da diese zudem von Faktoren wie der Distanz zwischen Server und Client sowie der Netzwerkauslastung beeinflusst wird.
Um diese Herausforderungen zu bewĂ€ltigen, wird eine Kombination verschiedener Techniken fĂŒr die Anpassung der einzelnen Verarbeitungsschritte der Visualisierungspipeline benötigt, angefangen bei effizienter DatenreprĂ€sentation bis hin zu hardware-beschleunigtem Rendering auf der Client-Seite. Diese Doktorarbeit befasst sich zunĂ€chst mit verwandten Arbeiten auf dem Gebiet der Remote-Visualisierung mit besonderem Fokus auf interaktiver web-basierter Visualisierung und prĂ€sentiert danach Techniken fĂŒr die interaktive Visualisierung im Browser mit Hilfe moderner Web-Standards wie WebGL und HTML5. Diese Techniken ermöglichen die Visualisierung dynamischer molekularer DatensĂ€tze mit mehr als einer Million Atomen bei interaktiven Frameraten durch die Verwendung GPU-basierten Raycastings. Aufgrund der EinschrĂ€nkungen, welche in einer Browser-basierten Umgebung vorliegen, musste die konkrete Implementierung des GPU-basierten Raycastings angepasst werden. Die Evaluation der daraus resultierenden Performanz zeigt, dass GPU-basierte Techniken das interaktive Rendering von groĂen DatensĂ€tzen ermöglichen und eine im Vergleich zu Polygon-basierten Techniken höhere BildqualitĂ€t erreichen.
Zur Verringerung der Ăbertragungszeiten, Reduktion der Latenz und Verbesserung der Darstellungsgeschwindigkeit werden effiziente AnsĂ€tze zur DatenreprĂ€sentation und ĂŒbertragung verwendet. Des Weiteren wird in dieser Doktorarbeit eine GPU-basierte Volumen-Ray-Marching-Technik auf Basis von WebGL 2.0 eingefĂŒhrt, welche progressive blockweise DatenĂŒbertragung verwendet, sowie verschiedene Detailgrade, um ein interaktives Volumenrendering von auf dem Server gespeicherten DatensĂ€tzen zu erreichen.
Die in dieser Doktorarbeit prĂ€sentierten Konzepte und Resultate tragen zur weiteren Verbreitung von interaktiver web-basierter Visualisierung bei. Die erzielten algorithmischen und technologischen Fortschritte bilden eine Grundlage fĂŒr weiterfĂŒhrende Entwicklungen von interaktiven Visualisierungsanwendungen auf Browser-Basis. Gleichzeitig hat dieser Ansatz das Potential, zukĂŒnftig kollaborative Visualisierung in der Cloud zu ermöglichen
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A Programmable Streaming Framework for Extreme-Scale Scientific Visualizations
Emerging computational and acquisition technologies are empowering scientists to conduct simulations and experiments on an unprecedented scale. These advancements can push the frontiers of science and technology with groundbreaking discoveries. However, they also pose significant challenges to traditional scientific visualization workflows. Firstly, the data generated by modern scientific studies using these technologies tends to be extremely large and complex, often resulting in slow processing and rendering times. This demands the development of visualization algorithms that can effectively scale with the size of the data. Secondly, state-of-the-art simulations and experiments produce data at extraordinary rates, complicating the task of generating valuable visualization results for scientists. Therefore, there's a pressing need for more adaptive and intelligent visualization workflows. Lastly, although new computer hardware and architecture can speed up the visualization process, significant performance variations still exist among visualization algorithms due to differing design choices. As a result, optimizing algorithms to better leverage emerging hardware features for enhanced efficiency remains an ongoing necessity.This dissertation addresses the aforementioned challenges by introducing a programmable streaming framework enhanced with implicit neural representation, designed for visualizing extreme-scale scientific data. Specifically, it unfolds three innovative methodologies:Firstly, the framework offers a reactive and declarative programming language for streamlining image generation, layout and interaction creation, and I/O processes, eliminating the need for users to manually control all visualization parameters and procedures. This language enables scientists to define highly adaptive visualization workflows through high-level, rule-based grammars. The system then automatically optimizes the low-level implementation according to these specifications, facilitating the creation of more efficient visualization workflows with simpler coding.Secondly, the framework features a scalable, hardware-accelerated streaming visualization system that allows visualization processes to run concurrently with I/O operations. This system not only achieves state-of-the-art scalability but can also effectively manages complex, multi-resolution data structures. It delivers accurate rendering outcomes, reduces memory usage, and leverages emerging hardware capabilities more efficiently.Finally, the framework integrates implicit neural representation (INR) techniques for data compression and interactive visualization. The use of INRs significantly reduces data size while preserving high-frequency details. Additionally, it enables direct access to spatial locations at any desired resolution, obviating the need for decompression or interpolation.In summary, this dissertation research addresses long-standing challenges inherent in extreme-scale scientific visualization by introducing novel designs and methodologies. The presented framework not only enables more efficient and adaptive visualization workflows but also leverages the latest hardware acceleration and data compression techniques. The implications of these advancements extend beyond mere technical improvements; they pave the way for deeper insights and discoveries across a broad spectrum of scientific studies. This research, therefore, represents a significant leap forward, with the potential to transform the landscape of scientific visualization
Development and Specification of Virtual Environments
This thesis concerns the issues involved in the development of virtual environments (VEs). VEs are more than virtual reality. We identify four main characteristics of them: graphical interaction, multimodality, interface agents, and multi-user. These characteristics are illustrated with an overview of different classes of VE-like applications, and a number of state-of-the-art VEs. To further define the topic of research, we propose a general framework for VE systems development, in which we identify five major classes of development tools: methodology, guidelines, design specification, analysis, and development environments. Of each, we give an overview of existing best practices