Abstract We present TreeON, a novel neural‐based framework for reconstructing detailed 3D tree point clouds from sparse top‐down geodata, using only a single orthophoto and its corresponding Digital Surface Model (DSM). Our method introduces a new training supervision strategy that combines both geometric supervision and a differentiable shadow and silhouette loss to learn point cloud representations of trees without requiring species labels, procedural rules, detailed terrestrial reconstruction data, or ground laser scan data. To address the lack of ground truth data, we generate a synthetic dataset of point clouds from procedurally modeled trees and train our network on it. Quantitative and qualitative experiments demonstrate better reconstruction quality and coverage compared to existing methods, as well as strong generalization to real‐world data, leading to visually appealing and structurally plausible tree point cloud representations that can be integrated into interactive digital 3D maps. The codebase, synthetic dataset, and pretrained model are publicly available at https://angelikigram.github.io/treeON/ .

Large-scale vision-language models (VLMs) such as CLIP have gained popularity for their generalizable and expressive multimodal representations. By leveraging large-scale training data with diverse textual metadata, VLMs acquire open-vocabulary capabilities, solving tasks beyond their training scope. This paper investigates the temporal awareness of VLMs, assessing their ability to position visual content in time. We introduce TIME10k, a benchmark dataset of over 10,000 images with temporal ground truth, and evaluate the time-awareness of 37 VLMs by a novel methodology. Our investigation reveals that temporal information is structured along a low-dimensional, non-linear manifold in the VLM embedding space. Based on this insight, we propose methods to derive an explicit ``timeline'' representation from the embedding space. These representations model time and its chronological progression and thereby facilitate temporal reasoning tasks. Our timeline approaches achieve competitive to superior accuracy compared to a prompt-based baseline while being computationally efficient. All code and data are available at https://tekayanidham.github.io/timeline-page/.

Large unlabeled datasets demand efficient and scalable data labeling solutions, in particular when the number of instances and classes is large. This leads to significant visual scalability challenges and imposes a high cognitive load on the users. Traditional instance-centric labeling methods, where (single) instances are labeled in each iteration struggle to scale effectively in these scenarios. To address these challenges, we introduce cVIL, a Class-Centric Visual Interactive Labeling methodology designed for interactive visual data labeling. By shifting the paradigm from assigning-classes-to-instances to assigning-instances-to-classes , cVIL reduces labeling effort and enhances efficiency for annotators working with large, complex and class-rich datasets. We propose a novel visual analytics labeling interface built on top of the conceptual cVIL workflow, enabling improved scalability over traditional visual labeling. In a user study, we demonstrate that cVIL can improve labeling efficiency and user satisfaction over instance-centric interfaces. The effectiveness of cVIL is further demonstrated through a usage scenario, showcasing its potential to alleviate cognitive load and support experts in managing extensive labeling tasks efficiently.

Abstract Bias in generative Text‐to‐Image (T2I) models is a known issue, yet systematically analyzing such models' outputs to uncover it remains challenging. We introduce the Visual Bias Explorer (ViBEx) to interactively explore the output space of T2I models to support the discovery of visual bias. ViBEx introduces a novel flexible prompting tree interface in combination with zero‐shot bias probing using CLIP for quick and approximate bias exploration. It additionally supports in‐depth confirmatory bias analysis through visual inspection of forward, intersectional, and inverse bias queries. ViBEx is model‐agnostic and publicly available. In four case study interviews, experts in AI and ethics were able to discover visual biases that have so far not been described in literature.

Selecting targets in dense, dynamic 3D environments presents a significant challenge. In this study, we introduce two novel selection techniques based on distractor pruning to assist users in selecting targets moving unpredictably: BaggingHook and AutoBaggingHook. Both are built upon the Hook intention-prediction heuristic, which continuously measures the distance between the user’s cursor and each object to compute per-object scores and estimate the intended target. Our techniques reduce the number of targets in the environment, making heuristic convergence potentially faster. Once pruned away, distractors are also made semi-transparent to reduce occlusion and the overall difficulty of the task. However, their motion is not altered, so that users can still perceive the dynamics of the environment. We designed two pruning approaches: BaggingHook lets users manually prune distractors away, while AutoBaggingHook uses automated, score-based pruning. We conducted a user study in a virtual reality setting inspired by molecular dynamics simulations, featuring crowded scenes of objects moving fast and unpredictably, in 3D. We compared both proposed techniques to the Hook baseline under more challenging circumstances than it had previously been tested. Our results show that AutoBaggingHook was the fastest, and did not lead to higher error rates. BaggingHook, on the other hand, was preferred by the majority of participants, due to the greater degree of control it provides to users, leading some to see entertainment value in its use. This work shows the potential benefits of varying the types of inputs used in intention-prediction heuristics, not just to improve performance, but also to reduce occlusion, overall task load, and improve user experience.

Abstract We conducted a study to systematically investigate the communication of complex dynamic processes along a two‐dimensional design space, where the axes represent a representation's manifestation (physical or virtual) and operation (manual or automatic). We exemplify the design space on a model embodying cardiovascular pathologies, represented by a mechanism where a liquid is pumped into a draining vessel, with complications illustrated through modifications to the model. The results of a mixed‐methods lab study with 28 participants show that both physical manifestation and manual operation have a strong positive impact on the audience's engagement. The study does not show a measurable knowledge increase with respect to cardiovascular pathologies using manually operated physical representations. However, subjectively, participants report a better understanding of the process—mainly through non‐visual cues like haptics, but also auditory cues. The study also indicates an increased task load when interacting with the process, which, however, seems to play a minor role for the participants. Overall, the study shows a clear potential of physicalization for the communication of complex dynamic processes, which only fully unfold if observers have to chance to interact with the process.

Abstract 3D animations are an effective method to learn about complex dynamic phenomena, such as mesoscale biological processes. The animators' goals are to convey a sense of the scene's overall complexity while, at the same time, visually guiding the user through a story of subsequent events embedded in the chaotic environment. Animators use a variety of visual emphasis techniques to guide the observers' attention through the story, such as highlighting, halos – or by manipulating motion parameters of the scene. In this paper, we investigate the effect of smoothing the motion of contextual scene elements to attract attention to focus elements of the story exhibiting high‐frequency motion. We conducted a crowdsourced study with 108 participants observing short animations with two illustrative motion smoothing strategies: geometric smoothing through noise reduction of contextual motion trajectories and visual smoothing through motion blur of context items. We investigated the observers' ability to follow the story as well as the effect of the techniques on speed perception in a molecular scene. Our results show that moderate motion blur significantly improves users' ability to follow the story. Geometric motion smoothing is less effective but increases the visual appeal of the animation. However, both techniques also slow down the perceived speed of the animation. We discuss the implications of these results and derive design guidelines for animators of complex dynamic visualizations.

Immersive virtual reality environments are gaining popularity for studying and exploring crowded three-dimensional structures. When reaching very high structural densities, the natural depiction of the scene produces impenetrable clutter and requires visibility and occlusion management strategies for exploration and orientation. Strategies developed to address the crowdedness in desktop applications, however, inhibit the feeling of immersion. They result in nonimmersive, desktop-style outside-in viewing in virtual reality. This article proposes Nanotilus-a new visibility and guidance approach for very dense environments that generates an endoscopic inside-out experience instead of outside-in viewing, preserving the immersive aspect of virtual reality. The approach consists of two novel, tightly coupled mechanisms that control scene sparsification simultaneously with camera path planning. The sparsification strategy is localized around the camera and is realized as a multi-scale, multi-shell, variety-preserving technique. When Nanotilus dives into the structures to capture internal details residing on multiple scales, it guides the camera using depth-based path planning. In addition to sparsification and path planning, we complete the tour generation with an animation controller, textual annotation, and text-to-visualization conversion. We demonstrate the generated guided tours on mesoscopic biological models - SARS-CoV-2 and HIV. We evaluate the Nanotilus experience with a baseline outside-in sparsification and navigational technique in a formal user study with 29 participants. While users can maintain a better overview using the outside-in sparsification, the study confirms our hypothesis that Nanotilus leads to stronger engagement and immersion.

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Similarity maps show dimensionality-reduced activation vectors of a high number of data points and thereby can help to understand which features a neural network has learned from the data. However, similarity maps have severely limited expressiveness for large datasets with hundreds of thousands of data instances and thousands of labels, such as ImageNet or word2vec. In this work, we present “concept splatters” as a scalable method to interactively explore similarities between data instances as learned by the machine through the lens of human-understandable semantics. Our approach enables interactive exploration of large latent spaces on multiple levels of abstraction. We present a web-based implementation that supports interactive exploration of tens of thousands of word vectors of word2vec and CNN feature vectors of ImageNet. In a qualitative study, users could effectively discover spurious learning strategies of the network, ambiguous labels, and could characterize reasons for potential confusion.

Abstract While convolutional neural networks (CNNs) have found wide adoption as state‐of‐the‐art models for image‐related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web‐based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine‐tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users’ insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models.

Abstract To exploit the potential of immersive network analytics for engaging and effective exploration, we promote the metaphor of “Egocentrism”, where data depiction and interaction are adapted to the perspective of the user within a 3D network. Egocentrism has the potential to overcome some of the inherent downsides of virtual environments, e.g., visual clutter and cyber‐sickness. To investigate the effect of this metaphor on immersive network exploration, we designed and evaluated interfaces of varying degrees of Egocentrism. In a user study, we evaluated the effect of these interfaces on visual search tasks, efficiency of network traversal, spatial orientation, as well as cyber‐sickness. Results show that a simple Egocentric interface considerably improves visual search efficiency and navigation performance, yet does not decrease spatial orientation or increase cyber‐sickness. An occlusion‐free Ego‐Bubble view of the neighborhood only marginally improves the user's performance. We tie our findings together in an open online tool for Egocentric network exploration, providing actionable insights on the benefits of the Egocentric network exploration metaphor.

Building-up and running a university-based research group is a multi-faceted undertaking. The visualization working group at TU Wien (vis-group) has been internationally active over more than 25 years. The group has been acting in a competitive scientific setting where sometimes contradicting multiple objectives require trade-offs and optimizations. Research-wise the group has been performing basic and applied research in visualization and visual computing. Teaching-wise the group has been involved in undergraduate and graduate lecturing in (medical) visualization and computer graphics. To be scientifically competitive requires to constantly expose the group and its members to a strong international competition at the highest level. This necessitates to shield the members against the ensuing pressures and demands and provide (emotional) support and encouragement. Internally, the vis-group has developed a unique professional and social interaction culture: work and celebrate, hard and together. This has crystallized into a nested, recursive, and triangular organization model, which concretizes what it takes to make a research group successful. The key elements are the creative and competent vis-group members who collaboratively strive for (scientific) excellence in a socially enjoyable environment.

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts - even for visualizing periodical daily patterns.

Abstract Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio‐temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.

Visualizations of hierarchical data can often be explored interactively. For example, in geographic visualization, there are continents, which can be subdivided into countries, states, counties and cities. Similarly, in models of viruses or bacteria at the highest level are the compartments, and below that are macromolecules, secondary structures (such as α-helices), amino-acids, and on the finest level atoms. Distinguishing between items can be assisted through the use of color at all levels. However, currently, there are no hierarchical and adaptive color mapping techniques for very large multi-scale visualizations that can be explored interactively. We present a novel, multi-scale, color-mapping technique for adaptively adjusting the color scheme to the current view and scale. Color is treated as a resource and is smoothly redistributed. The distribution adjusts to the scale of the currently observed detail and maximizes the color range utilization given current viewing requirements. Thus, we ensure that the user is able to distinguish items on any level, even if the color is not constant for a particular feature. The coloring technique is demonstrated for a political map and a mesoscale structural model of HIV. The technique has been tested by users with expertise in structural biology and was overall well received.

Abstract When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set‐based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set‐based text visualization techniques adopted for visualizing expanded query results – namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View – to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set‐based text visualization techniques in the context of web search.

Drawing the user's gaze to an important item in an image or a graphical user interface is a common challenge. Usually, some form of highlighting is used, such as a clearly distinct color or a border around the item. Flicker can also be very salient, but is often perceived as annoying. In this paper, we explore high frequency flicker (60 to 72 Hz) to guide the user's attention in an image. At such high frequencies, the critical flicker frequency (CFF) threshold is reached, which makes the flicker appear to fuse into a stable signal. However, the CFF is not uniform across the visual field, but is higher in the peripheral vision at normal lighting conditions. Through experiments, we show that high frequency flicker can be easily detected by observers in the peripheral vision, but the signal is hardly visible in the foveal vision when users directly look at the flickering patch. We demonstrate that this property can be used to draw the user's attention to important image regions using a standard high refresh-rate computer monitor with minimal visible modifications to the image. In an uncalibrated visual search task, users could in a crowded image easily spot the specified search targets flickering with very high frequency. They also reported that high frequency flicker was distracting when they had to attend to another region, while it was hardly noticeable when looking at the flickering region itself.

Animated movies are a popular way to communicate complex phenomena in cell biology to the broad audience. Animation artists apply sophisticated illustration techniques to communicate a story, while trying to maintain a realistic representation of a complex dynamic environment. Since such hand-crafted animations are time-consuming and cost-intensive to create, our goal is to formalize illustration techniques used by artists to facilitate the automatic creation of visualizations generated from mesoscale particle-based molecular simulations. Our technique Illustrative Timelapse supports visual exploration of complex biochemical processes in dynamic environments by (1) seamless temporal zooming to observe phenomena in different temporal resolutions, (2) visual abstraction of molecular trajectories to ensure that observers are able to visually follow the main actors, (3) increased visual focus on events of interest, and (4) lens effects to preserve a realistic representation of the environment in the context. Results from a first user study indicate that visual abstraction of trajectories improves the ability to follow a story and is also appreciated by users. Lens effects increased the perceived amount of molecular motion in the environment while trading off traceability of individual molecules.

Focus+context techniques provide visual guidance in visualizations by giving strong visual prominence to elements of interest while the context is suppressed. However, finding a visual feature to enhance for the focus to pop out from its context in a large dynamic scene, while leading to minimal visual deformation and subjective disturbance, is challenging. This paper proposes Attractive Flicker, a novel technique for visual guidance in dynamic narrative visualizations. We first show that flicker is a strong visual attractor in the entire visual field, without distorting, suppressing, or adding any scene elements. The novel aspect of our Attractive Flicker technique is that it consists of two signal stages: The first "orientation stage" is a short but intensive flicker stimulus to attract the attention to elements of interest. Subsequently, the intensive flicker is reduced to a minimally disturbing luminance oscillation ("engagement stage") as visual support to keep track of the focus elements. To find a good trade-off between attraction effectiveness and subjective annoyance caused by flicker, we conducted two perceptual studies to find suitable signal parameters. We showcase Attractive Flicker with the parameters obtained from the perceptual statistics in a study of molecular interactions. With Attractive Flicker, users were able to easily follow the narrative of the visualization on a large display, while the flickering of focus elements was not disturbing when observing the context.

Abstract In this paper, we present the design and implementation of a dynamic window management technique that changes the perception of windows as fixed‐sized rectangles. The primary goal of self‐organizing windows is to automatically display the most relevant information for a user's current activity, which removes the burden of organizing and arranging windows from the user. We analyze the image‐based representation of each window and identify coherent pieces of information. The windows are then automatically moved, scaled and composed in a content‐aware manner to fit the most relevant information into the limited area of the screen. During the design process, we consider findings from previous experiments and show how users can benefit from our system. We also describe how the immense processing power of current graphics processing units can be exploited to build an interactive system that finds an optimal solution within the complex design space of all possible window transformations in real time.

Information exploration processes are often conducted in teams of experts, family members, or colleagues. These teams have to retrieve information from different sources, verify it, and finally compare and discuss their findings to find consensus. Today, support for these collaborative processes is limited and users often end up sharing either a single PC with one user taking control or using separate workstations, where support for tight collaboration is limited. In this paper, we present collaborative information linking which visually connects information across private and shared application windows to bridge knowledge gaps between users. We present the technical infrastructure for multi-user interaction and personalized meta-visualizations on large multi-projector displays, and demonstrate how personalized visual links connect information across existing applications modified in a minimally invasive manner. An observational experiment showed that information linking helps individuals to deal with large display space and teams to switch between individual information retrieval and joint verification and discussion.

In this paper we present importance-driven compositing window management, which considers windows not only as basic rectangular shapes but also integrates the importance of the windows' content using a bottom-up visual attention model. Based on this information, importance-driven compositing optimizes the spatial window layout for maximum visibility and interactivity of occluded content in combination with see-through windows. We employ this technique for emerging window manager functions to minimize information overlap caused by popping up windows or floating toolbars and to improve the access to occluded window content. An initial user study indicates that our technique provides a more effective and satisfactory access to occluded information than the well-adopted Alt+Tab window switching technique and see-through windows without optimized spatial layout.

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.

It is now increasingly common to extend private workstations with large public displays into a shared multi-display environment (MDE). Mouse-based interaction across multiple displays provides a convenient way to quickly shift between private work on the personal monitor and tightly coupled collaboration on shared display spaces (Figure 1). However, mouse pointer navigation can be negatively influenced by display factors in the environment and thereby limit fluid interaction across displays. We report findings from an experiment comparing four mouse pointer navigation techniques in a heterogeneous MDE.

Multi-display environments combine displays of various form factors into a common interaction space. Cross-display navigation techniques have to provide transitions to move the mouse pointer across display boundaries to reach distant display locations. A spatially consistent description of display relationships thereby supports fluid cross-display navigation. In this paper, we present two spatially consistent navigation techniques for seamless cross-display navigation in multi-user multi-display environments. These navigation techniques are automatically configured from a spatial model of the environment, which is generated in a camera-assisted calibration step. We describe the implementation in a distributed system and present results of a comparative experiment.

In this paper we present the multi-display environment Deskotheque, which combines personal and tiled projected displays into a continuous teamspace. Its main distinguishing factor is a fine-grained spatial (i. e., both geometric and topological) model of the display layout. Using this model, Deskotheque allows seamless mouse pointer navigation and application window sharing across the multi-display environment. Geometric compensation of casually aligned multi-projector displays supports a wide range of display configurations. Mouse pointer redirection and window migration are tightly integrated into the windowing system, while geometric compensation of projected imagery is accomplished by a 3D compositing window manager. Thus, Deskotheque provides sharing of unmodified desktop application windows across display and workstation boundaries without compromising hardware-accelerated rendering of 2D or 3D content on projected tiled displays with geometric compensation.