Scientific Visualization Applications Research Articles

2023 IEEE Scientific Visualization Contest Winner: VisAnywhere: Developing Multi-platform Scientific Visualization Applications.

Scientists often explore and analyze large-scale scientific simulation data by leveraging two- and three-dimensional visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain provided for the 2023 IEEE Scientific Visualization Contest, we present our work leveraging various web technologies to create a multi-platform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both co-located and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.

Image Morpher

The "Image Morphing with the Beier-Neely Method" research paper extensively explores image transformation techniques, with a special focus on the Beier- Neely approach. Image morphing, the art of seamlessly transforming one image into another, has garnered significant attention for its applications in animation, filmmaking, and scientific visualization. The research paper delves into the historical context and importance of image morphing, particularly emphasizing the role of the Beier-Neely method in addressing the limitations of earlier techniques. Classical image morphing methods, characterized by pixel-level interpolation, suffer from visual artifacts, manual correspondence definition, and sluggishness. These shortcomings set the stage for the introduction of the feature-based Beier-Neely method, which revolutionized image morphing by allowing control points on features, facilitating smooth transitions and natural transformations. This research papers objectives are clearly articulated, with the primary aim of implementing the Beier- Neely method to produce high-quality image morphs. The research paper proposes a system architecture that underscores the use of feature-based control points, Delaunay triangulation, affine transformations, and the significance of visual feedback. Additionally, it provides a detailed analysis of the mathematical foundations and algorithmic intricacies of the Beier-Neely method, elucidating the theoretical framework driving its effectiveness. Overall, the research paper provides a comprehensive exploration of image morphing techniques, highlighting the pivotal role played by the Beier-Neely method in advancing the field.. Key Words: Image Morphing, Beier-Neely Method, Seamless Transitions, Feature-based Control Points, System Architecture, Mathematical Foundations

Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network.

Feature grid Scene Representation Networks (SRNs) have been applied to scientific data as compact functional surrogates for analysis and visualization. As SRNs are black-box lossy data representations, assessing the prediction quality is critical for scientific visualization applications to ensure that scientists can trust the information being visualized. Currently, existing architectures do not support inference time reconstruction quality assessment, as coordinate-level errors cannot be evaluated in the absence of ground truth data. By employing the uncertain neural network architecture in feature grid SRNs, we obtain prediction variances during inference time to facilitate confidence-aware data reconstruction. Specifically, we propose a parameter-efficient multi-decoder SRN (MDSRN) architecture consisting of a shared feature grid with multiple lightweight multi-layer perceptron decoders. MDSRN can generate a set of plausible predictions for a given input coordinate to compute the mean as the prediction of the multi-decoder ensemble and the variance as a confidence score. The coordinate-level variance can be rendered along with the data to inform the reconstruction quality, or be integrated into uncertainty-aware volume visualization algorithms. To prevent the misalignment between the quantified variance and the prediction quality, we propose a novel variance regularization loss for ensemble learning that promotes the Regularized multi-decoder SRN (RMDSRN) to obtain a more reliable variance that correlates closely to the true model error. We comprehensively evaluate the quality of variance quantification and data reconstruction of Monte Carlo Dropout (MCD), Mean Field Variational Inference (MFVI), Deep Ensemble (DE), and Predicting Variance (PV) in comparison with our proposed MDSRN and RMDSRN applied to state-of-the-art feature grid SRNs across diverse scalar field datasets. We demonstrate that RMDSRN attains the most accurate data reconstruction and competitive variance-error correlation among uncertain SRNs under the same neural network parameter budgets. Furthermore, we present an adaptation of uncertainty-aware volume rendering and shed light on the potential of incorporating uncertain predictions in improving the quality of volume rendering for uncertain SRNs. Through ablation studies on the regularization strength and decoder count, we show that MDSRN and RMDSRN are expected to perform sufficiently well with a default configuration without requiring customized hyperparameter settings for different datasets.

Branch Decomposition‐Independent Edit Distances for Merge Trees

AbstractEdit distances between merge trees of scalar fields have many applications in scientific visualization, such as ensemble analysis, feature tracking or symmetry detection. In this paper, we propose branch mappings, a novel approach to the construction of edit mappings for merge trees. Classic edit mappings match nodes or edges of two trees onto each other, and therefore have to either rely on branch decompositions of both trees or have to use auxiliary node properties to determine a matching. In contrast, branch mappings employ branch properties instead of node similarity information, and are independent of predetermined branch decompositions. Especially for topological features, which are typically based on branch properties, this allows a more intuitive distance measure which is also less susceptible to instabilities from small‐scale perturbations. For trees with 𝒪(n) nodes, we describe an 𝒪(n4) algorithm for computing optimal branch mappings, which is faster than the only other branch decomposition‐independent method in the literature by more than a linear factor. Furthermore, we compare the results of our method on synthetic and real‐world examples to demonstrate its practicality and utility.

SurfNet: Learning Surface Representations via Graph Convolutional Network

AbstractFor scientific visualization applications, understanding the structure of a single surface (e.g., stream surface, isosurface) and selecting representative surfaces play a crucial role. In response, we propose SurfNet, a graph‐based deep learning approach for representing a surface locally at the node level and globally at the surface level. By treating surfaces as graphs, we leverage a graph convolutional network to learn node embedding on a surface. To make the learned embedding effective, we consider various pieces of information (e.g., position, normal, velocity) for network input and investigate multiple losses. Furthermore, we apply dimensionality reduction to transform the learned embeddings into 2D space for understanding and exploration. To demonstrate the effectiveness of SurfNet, we evaluate the embeddings in node clustering (node‐level) and surface selection (surface‐level) tasks. We compare SurfNet against state‐of‐the‐art node embedding approaches and surface selection methods. We also demonstrate the superiority of SurfNet by comparing it against a spectral‐based mesh segmentation approach. The results show that SurfNet can learn better representations at the node and surface levels with less training time and fewer training samples while generating comparable or better clustering and selection results.

IDLat: An Importance-Driven Latent Generation Method for Scientific Data.

Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent representations are mostly generated from raw data in an unsupervised manner, which makes it difficult to incorporate domain interest to control the size of the latent representations and the quality of the reconstructed data. In this paper, we present a novel importance-driven latent representation to facilitate domain-interest-guided scientific data visualization and analysis. We utilize spatial importance maps to represent various scientific interests and take them as the input to a feature transformation network to guide latent generation. We further reduced the latent size by a lossless entropy encoding algorithm trained together with the autoencoder, improving the storage and memory efficiency. We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.

Post-processing for ITER Scrape-Off Layer Plasma Simulations (SOLPS-ITER) in IMAS Framework

With the ongoing effort to establish the Integrated Modelling and Analysis Suite (IMAS) and the Interface Data Structures (IDSs) as the standard relevant software infrastructure for ITER Organization and its parties for all ITER related activities, we report on steps towards the integration of the B2.5-Eirene plasma fluid solver, one of the codes in the SOLPS-ITER code package, within IMAS workflows, by means of a set of modules which allow post-processing of solution data into the IMAS model. This way, after a case is successfully completed, the solver simulation output results can be stored to the IDSs. To provide the means for visualization of the edge plasma state from the IDSs, a plugin for ParaView, an open-source scientific data analysis and visualization application, named ReadUALEdge was developed. With the data displayed within the ParaView application, all tools and utilities provided by ParaView can be used on the data for convenient and efficient analysis. In this article, we give an overview of the IMAS framework and SOLPS-ITER status and present the main aspects and features of the latest ReadUALEdge plugin for the ParaView application.

On Evaluating Runtime Performance of Interactive Visualizations.

As our field matures, evaluation of visualization techniques has extended from reporting runtime performance to studying user behavior. Consequently, many methodologies and best practices for user studies have evolved. While maintaining interactivity continues to be crucial for the exploration of large data sets, no similar methodological foundation for evaluating runtime performance has been developed. Our analysis of 50 recent visualization papers on new or improved techniques for rendering volumes or particles indicates that only a very limited set of parameters like different data sets, camera paths, viewport sizes, and GPUs are investigated, which make comparison with other techniques or generalization to other parameter ranges at least questionable. To derive a deeper understanding of qualitative runtime behavior and quantitative parameter dependencies, we developed a framework for the most exhaustive performance evaluation of volume and particle visualization techniques that we are aware of, including millions of measurements on ten different GPUs. This paper reports on our insights from statistical analysis of this data, discussing independent and linear parameter behavior and non-obvious effects. We give recommendations for best practices when evaluating runtime performance of scientific visualization applications, which can serve as a starting point for more elaborate models of performance quantification.

Animated mesh simplification based on motion features in visual sensor networks

The three-dimensional animated model is widely used in scientific visualization, entertainment, and virtual applications, especially in visual sensor networks. The main purpose of simplification is to capture the shape sequence of an object with very few elements while preserving the overall shape. As three-dimensional animated mesh is time-varying in all frames, the trade-off between the temporal coherence and geometric distortion must be considered to develop simplification algorithm. In this article, a novel three-dimensional animated mesh simplification algorithm based on motion features is presented. Here, motion features are the connection areas of the relative movement consisted of vertices and edges. Motion feature extraction is to find a subgraph that has movement property. Dihedral angle of the edge through all frames is used to determine whether an edge is connected or not to the movement parts. Then, a rotation connected graph is defined to extract motion features. Traveling this graph, all motion features can be extracted. Based on the motion features, animated quadric error metric is created and quadric error matrix is built through all frames. Compared with the other methods, the important advantages of this method are high-efficiency simplification process and smoother simplification effects. It is suitable to be used in real-time applications. Experiment results show that the 3D animated mesh simplification effects by our method are satisfactory.

Scientific Visualization

This issue of CGaA contains several articles describing research advances in scientific visualization contextualized by established as well as prototypical toolkits and systems. This selection of visualization-systems-related articles not only provides a glimpse into the challenges of domain-specific scientific visualization applications, but also depicts an interesting cross section of computer graphics and interaction challenges associated with those applications and related solutions that might have broader applicability.

Modulight: A Framework for Efficient Dynamic Interactive Scientific Visualization

The interactive scientific visualization applications are based on heterogeneous codes to implement simulation or data processing, visualization and interaction parts. These different parts need to be precisely assembled to construct an efficient application running in interactive time. Component-based approach is a good paradigm to express this kind of applications. The interactive scientific visualization domain is now classically extended with visual analysis applications. In this case, some parts of the application need to be added or removed dynamically during its execution. In this paper, we describe a component-based approach dedicated to dynamic interactive scientific visualization applications. We propose a framework called Modulight which implements our approach using the MPI2 library and the optimized socket library ØMQ. The performance of this framework is also analyzed from a real-life application of molecular dynamics.

IMMERSIVE VR VISUALIZATIONS BY VFIVE PART 2: APPLICATIONS

VFIVE is a scientific visualization application for CAVE-type immersive virtual reality (VR) systems. The source codes are freely available. VFIVE is used as a research tool in various VR systems. It also lays the groundwork for developments of new visualization software for CAVEs. In this paper, we pick up five CAVE systems in four different institutions in Japan. Applications of VFIVE in each CAVE system are summarized. Special emphases will be placed on scientific and technical achievements made possible by VFIVE.

Phase-based video motion processing

We introduce a technique to manipulate small movements in videos based on an analysis of motion in complex-valued image pyramids. Phase variations of the coefficients of a complex-valued steerable pyramid over time correspond to motion, and can be temporally processed and amplified to reveal imperceptible motions, or attenuated to remove distracting changes. This processing does not involve the computation of optical flow, and in comparison to the previous Eulerian Video Magnification method it supports larger amplification factors and is significantly less sensitive to noise. These improved capabilities broaden the set of applications for motion processing in videos. We demonstrate the advantages of this approach on synthetic and natural video sequences, and explore applications in scientific analysis, visualization and video enhancement.

Effects of Stereo and Screen Size on the Legibility of Three-Dimensional Streamtube Visualization.

We report the impact of display characteristics (stereo and size) on task performance in diffusion magnetic resonance imaging (DMRI) in a user study with 12 participants. The hypotheses were that (1) adding stereo and increasing display size would improve task accuracy and reduce completion time, and (2) the greater the complexity of a spatial task, the greater the benefits of an improved display. Thus we expected to see greater performance gains when detailed visual reasoning was required. Participants used dense streamtube visualizations to perform five representative tasks: (1) determine the higher average fractional anisotropy (FA) values between two regions, (2) find the endpoints of fiber tracts, (3) name a bundle, (4) mark a brain lesion, and (5) judge if tracts belong to the same bundle. Contrary to our hypotheses, we found the task completion time was not improved by the use of the larger display and that performance accuracy was hurt rather than helped by the introduction of stereo in our study with dense DMRI data. Bigger was not always better. Thus cautious should be taken when selecting displays for scientific visualization applications. We explored the results further using the body-scale unit and subjective size and stereo experiences.

Focus on software and semiconductors

Focus on software and semiconductors

Scientific Visualization in Astronomy: Towards the Petascale Astronomy Era

AbstractAstronomy is entering a new era of discovery, coincident with the establishment of new facilities for observation and simulation that will routinely generate petabytes of data. While an increasing reliance on automated data analysis is anticipated, a critical role will remain for visualization-based knowledge discovery. We have investigated scientific visualization applications in astronomy through an examination of the literature published during the last two decades. We identify the two most active fields for progress — visualization of large-N particle data and spectral data cubes—discuss open areas of research, and introduce a mapping between astronomical sources of data and data representations used in general-purpose visualization tools. We discuss contributions using high-performance computing architectures (e.g. distributed processing and GPUs), collaborative astronomy visualization, the use of workflow systems to store metadata about visualization parameters, and the use of advanced interaction devices. We examine a number of issues that may be limiting the spread of scientific visualization research in astronomy and identify six grand challenges for scientific visualization research in the Petascale Astronomy Era.

Highlights from IEEE Pacific Visualization.

This article looks briefly at four articles based on papers from the 2010 IEEE Pacific Visualization Symposium. These articles, which strongly focus on visual design and applications, cover a range of applications in scientific visualization, information visualization, and graph visualization, showing the field's breadth.

Multisensory VR exploration for computer fluid dynamics in the CoRSAIRe project

In the last 30 years, the evolution of digital data processing in terms of processing power, storage capacity, and algorithmic efficiency in the simulation of physical phenomena has allowed the emergence of the discipline known as computational fluid dynamics or CFD. More recently, virtual reality (VR) systems have proven an interesting alternative to conventional user interfaces, in particular, when exploring complex and massive datasets, such as those encountered in scientific visualization applications. Unfortunately, all too often, VR technologies have proven unsatisfactory in providing a true added value compared to standard interfaces, mostly because insufficient attention was given to the activity and needs of the intended user audience. The present work focuses on the design of a multimodal VR environment dedicated to the analysis of non-stationary flows in CFD. Specifically, we report on the identification of relevant strategies of CFD exploration coupled to adapted VR data representation and interaction techniques. Three different contributions will be highlighted. First, we show how placing the CFD expert user at the heart of the system is accomplished through a formalized analysis of work activity and through system evaluation. Second, auditory outputs providing analysis of time-varying phenomena in a spatialized virtual environment are introduced and evaluated. Finally, specific haptic feedbacks are designed and evaluated to enhance classical visual data exploration of CFD simulations.

Efficient algorithms for computing Reeb graphs

The Reeb graph tracks topology changes in level sets of a scalar function and finds applications in scientific visualization and geometric modeling. We describe an algorithm that constructs the Reeb graph of a Morse function defined on a 3-manifold. Our algorithm maintains connected components of the two dimensional levels sets as a dynamic graph and constructs the Reeb graph in O ( n log n + n log g ( log log g ) 3 ) time, where n is the number of triangles in the tetrahedral mesh representing the 3-manifold and g is the maximum genus over all level sets of the function. We extend this algorithm to construct Reeb graphs of d-manifolds in O ( n log n ( log log n ) 3 ) time, where n is the number of triangles in the simplicial complex that represents the d-manifold. Our result is a significant improvement over the previously known O ( n 2 ) algorithm. Finally, we present experimental results of our implementation and demonstrate that our algorithm for 3-manifolds performs efficiently in practice.

Personal Varrier: Autostereoscopic virtual reality display for distributed scientific visualization

As scientific data sets increase in size, dimensionality, and complexity, new high resolution, interactive, collaborative networked display systems are required to view them in real-time. Increasingly, the principles of virtual reality (VR) are being applied to modern scientific visualization. One of the tenets of VR is stereoscopic (stereo or 3d) display; however the need to wear stereo glasses or other gear to experience the virtual world is encumbering and hinders other positive aspects of VR such as collaboration. Autostereoscopic (autostereo) displays presented imagery in 3d without the need to wear glasses or other gear, but few qualify as VR displays. The Electronic Visualization Laboratory (EVL) at the University of Illinois at Chicago (UIC) has designed and built a single-screen version of its 35-panel tiled Varrier display, called Personal Varrier. Based on a static parallax barrier and the Varrier computational method, Personal Varrier provides a quality 3d autostereo experience in an economical, compact form factor. The system debuted at iGrid 2005 in San Diego, CA, accompanied by a suite of distributed and local scientific visualization and 3d teleconferencing applications. The CAVEwave National LambdaRail (NLR) network was vital to the success of the stereo teleconferencing.