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Differential Gene Expression Analysis with Visual Analytics
No full textDifferential gene expression (DGE) analysis is one of the most used techniques for RNA-seq data analysis, and it is applied in various medical and biological contexts, including biomarkers for diagnosis and prognosis and evaluation of the effectiveness of specific treatments. The conduction of a DGE analysis typically involves navigating a complex, multi-step pipeline, which usually requires proficiency in programming languages like R. This presents a barrier to researchers like biologists and clinicians, who may have limited or no coding skills, and adds additional overhead even for experienced bioinformaticians. To overcome these challenges, we propose a preliminary visual analytics prototype that simplifies DGE analysis, enabling users to perform the analyses without coding expertise.EuroVis 2025 - PostersPoster
LidarScout: Direct Out-of-Core Rendering of Massive Point Clouds
No full textLarge-scale terrain scans are the basis for many important tasks, such as topographic mapping, forestry, agriculture, and infrastructure planning. The resulting point cloud data sets are so massive in size that even basic tasks like viewing take hours to days of pre-processing in order to create level-of-detail structures that allow inspecting the data set in their entirety in real time. In this paper, we propose a method that is capable of instantly visualizing massive country-sized scans with hundreds of billions of points. Upon opening the data set, we first load a sparse subsample of points and initialize an overview of the entire point cloud, immediately followed by a surface reconstruction process to generate higher-quality, hole-free heightmaps. As users start navigating towards a region of interest, we continue to prioritize the heightmap construction process to the user's viewpoint. Once a user zooms in closely, we load the full-resolution point cloud data for that region and update the corresponding height map textures with the full-resolution data. As users navigate elsewhere, full-resolution point data that is no longer needed is unloaded, but the updated heightmap textures are retained as a form of medium level of detail. Overall, our method constitutes a form of direct out-of-core rendering for massive point cloud data sets (terabytes, compressed) that requires no preprocessing and no additional disk space. Source code, executable, pre-trained model, and dataset are available at: https://github.com/cg-tuwien/lidarscoutHigh-Performance Graphics - Symposium PapersSplats and Point
Using Smartphone EXIF Data to Classify Lighting Conditions for Outdoor Augmented Reality
No full textCorrectly matching real-world environment lighting conditions is an important step in making Augmented Reality content better fit with surrounding real objects. It is also the first step in larger, more complex problems like object relighting, shadow estimation, surface shading, etc. Dynamic classification of lighting conditions thus needs to be robust and lightweight. In this paper, we investigate the suitability of using pure EXIF data for classifying outdoor lighting conditions in four broad categories using a variety of shallow machine learning models. We gather a dataset of images together with EXIF metadata to test different models and show the results from the best-performing one in a real-time Augmented Reality application on a smartphone.Eurographics 2025 - PostersPoster
Robust Discrete Differential Operators for Wild Geometry
No full textMany geometry processing algorithms rely on solving PDEs on discrete surface meshes. Their accuracy and robustness crucially depend on the mesh quality, which oftentimes cannot be guaranteed - in particular when automatically processing geometries extracted from arbitrary implicit representations. Through extensive numerical experiments, we evaluate the robustness of various Laplacian implementations across geometry processing libraries on synthetic and ''in-the-wild'' surface meshes with degenerate or near-degenerate elements, revealing their strengths, weaknesses, and failure cases. To improve numerical stability, we extend the recently proposed tempered finite elements method (TFEM) to meshes with strongly varying element sizes, to arbitrary polygonal elements, and to gradient and divergence operators. Our resulting differential operators are simple to implement, efficient to compute, and robust even in the presence of fully degenerate mesh elements.Vision, Modeling, and VisualizationGeometry, Simulation, and Optimizatio
Full-fledged Virtual Exploration of Sacred Spaces
No full textVirtual Reality (VR) revolutionised the way the users can perceive virtual or distant spaces and interact with them. The users wearing VR headsets have the impression as if they would look around at the real place of the sights. The technology is widely used in entertainment and training. Our goal was to examine how the VR technology can be applied to present sacral spaces. We assembled the virtual reality model of a Bulgarian Orthodox church in a cost-effective manner from high-resolution spherical panorama pictures. A complex VR exploration platform was created by integrating our VR presentation tool with the digital repository of Bulgarian icons and iconographic objects. Due to the integration, additional information can be displayed on specific frescoes of the church in the VR space. We offer a full-fledged virtual exploration of sacred spaces where the experience of moving around the virtual space and exploring the details of the church can be combined with transferring lexical knowledge about the church and its selected objects. The first versions of our virtual walks were available on the Web only. Due to the multiplatform development methodology applied, virtual walks can be demonstrated on VR as well.Digital HeritageAccessibility and Inclusive Engagemen
Augury and Forerunner: Real-Time Feedback Via Predictive Numerical Optimization and Input Prediction
No full textIn many interactive systems, user input initializes and launches an iterative optimization procedure. The goal is to provide assistive feedback to some creation/editing process. Examples include constraint-based GUI layout and complex snapping scenarios. Many geometric problems, such as fitting a shape to data, involve optimizations which may take seconds to complete (or even longer), yet require human guidance. In order to make these optimization routines practical in interactive sessions, simplifications or sacrifices must be made. Canonically, non-convex optimization problems are solved iteratively by taking a series of steps towards a solution. By their nature, there are many locally optimal solutions; which solution is found is highly dependent on an initial guess. There is a fundamental conflict between optimization and interactivity. Interrupting and restarting the optimization every time the user, e.g. moves the mouse prevents any solution from being computed until the user ceases interaction. Continuing to run the optimization procedure computes a perpetually outdated solution. This presents a particular unsolved challenge with respect to direct manipulation. Every time the user, e.g. moves the mouse, the entire optimization must be re-started with the new user input, since returning a stale result associated with the previous user state is undesirable. We propose predictive short-circuiting to reduce this fundamental tension. Our approach memoizes paths in the optimization's configuration space and predicts the trajectory of future optimization in real time, leveraging common C1 continuity assumptions. This enables direct manipulation of formerly sluggish interactions. We demonstrate our approach on geometric fitting tasks. Additionally, we evaluate complementary mouse motion prediction algorithms as a means to discard or skip optimization problems that are irrelevant to the user's intended initial configuration for a targeted optimization procedure. Predicting where the mouse cursor will be located at the end of an operation, such as dragging a model of an engine component into scanned point cloud data to perform geometric alignment, allows us to pre-emptively begin solving the targeted problem before the user finishes their movement. We take advantage of the fact that the prediction indicates the approximate energy basin the optimization procedure will need to explore.Computer Graphics ForumOriginal Article44
Grasping Data Through Play: Exploring Co-Design Activities for Children's Engagement with Personal Data
No full textWith the increase of ''smart'' toys and other devices that collect personal data from children and their carers at home and/or in public places, questions of how to raise an awareness of the value of personal data in children and how to promote an active engagement of personal data for the purpose of self-awareness have become more pressing. To address this, the Grasping Data project explores the potential of playful visualization and physicalization activities-designed with and for children-to make their personal data visible and to promote an understanding of collecting and analyzing such data for children's own benefit. However, while research on personal data vis- and physicalization activities and bespoke toolkits exists, designing such activities for young children (3-8) and their carers is underexplored. At the same time, designing activities that focus on personal data comes with its own challenges: how ''personal'' data is defined in the first place, what are children's perspectives on these, and how can navigate ethical and privacy concerns in a constructive way with children and their carers? Building on this, in this workshop paper we introduce a play-based activity to explore how adults engage and interpret personal data through play and tangible visualizations. Through this activity, we aim to explore and discuss how adults define 'personal data' and the potential of play and visualizations to help them 'grasp' the meaning and value of their data.EuroVis Workshop on Visualization Play, Games, and ActivitiesPaper
Stereo Spectacular: Reviving the Universal Exposition of 1867 in Virtual Reality through Historical Stereoscopic Photographs
No full textStereo Spectacular unites 19th-century stereoscopic immersive media with contemporary virtual reality technologies, reconstructing the Exposition Universelle of Paris 1867 (UE1867) through archival stereoscopic photographs within an immersive museum experience. As a landmark historical event, the expo marked a turning point in visual culture. Part of the Expo's spectacular appeal involved the use of contemporary technologies in the service of museography and art. One such technology was stereography, which was used to extensively document the event through 1000+ stereoscopic photographs. Stereo Spectacular presents a navigable art-tectonic virtual reconstruction of the UE1867: hundreds of digitized, restored, geolocated, augmented, and sonified stereo pairs are configured into a navigable, map-based visual space that museum audiences can freely explore. Stereo Spectacular is built for a state-of-the-art multi-user 360° stereoscopic virtual reality system, but is scalable to any and all virtual reality and stereoscopic visualization hardware. The project presents a novel curatorial framework for reconstituting and reconfiguring stereo archives into compelling reconstructions of place and time. It demonstrates how image archives can become navigable VR places, and how multisensory augmentations can be used to shed new light on visions of the past. Importantly, the project transforms the individually viewed stereo pair into a socially experienced visual space, reinventing the historical stereo viewing experience, and - in the case of the UE1867 - reinterpreting the experience of visiting the fair through contemporary immersive visualization.Digital HeritageVisual Archives and Historical Imagery in V
Hybrid Retrieval-Regression for Motion-Driven Loose-Fitting Garment Animation
No full textWe present a hybrid retrieval-regression framework for motion-driven garment animation leveraging a shared discrete codebook. Our method targets the challenge of animating loose-fitting garments, whose dynamic behaviors exhibit high variability and less direct correlation with body motion-making them difficult to handle with conventional example-based approaches that assume tightly coupled motion-garment relationships. To address this, we project both motion and garment animation clips into a shared discrete codebook via Gumbel-Softmax-based quantization, allowing them to be aligned in a semantically consistent space where cross-retrieval can be performed using simple distance metrics. During inference, we adaptively switch between retrieval and regression based on the confidence derived from the codebook probability distribution, allowing the system to remain robust in the presence of ambiguous or unseen motions. We leverage a pre-trained mesh autoencoder to obtain garment latents that preserve local geometric structure, enabling smoother transitions and more geometrically consistent interpolation between retrieved and regressed animation segments efficiently. Experimental results demonstrate that our approach improves the accuracy and plausibility of garment animation for complex garments under diverse motion inputs, while maintaining robustness to unseen scenarios and achieving low simulation error for high-quality garment animation.Pacific Graphics Conference Papers, Posters, and DemosDigital Clothin
Mesh Compression with Quantized Neural Displacement Fields
No full textImplicit neural representations (INRs) have been successfully used to compress a variety of 3D surface representations such as Signed Distance Functions (SDFs), voxel grids, and also other forms of structured data such as images, videos, and audio. However, these methods have been limited in their application to unstructured data such as 3D meshes and point clouds. This work presents a simple yet effective method that extends the usage of INRs to compress 3D triangle meshes. Our method encodes a displacement field that refines the coarse version of the 3D mesh surface to be compressed using a small neural network. Once trained, the neural network weights occupy much lower memory than the displacement field or the original surface. We show that our method is capable of preserving intricate geometric textures and demonstrates state-of-the-art performance for compression ratios ranging from 4x to 380x (See Figure 1 for an example).Computer Graphics ForumGeometrically, Parametrically Speaking44