VMV2025

Permanent URI for this collection

Erlangen, Germany | September 29 - October 1, 2025
Real-Time Rendering
Alias-Free Shadows with Ray Cones for Alpha Tested Geometry
Felix Brüll, René Kern, and Thorsten Grosch
Fast Rendering of Large-Scale Dynamic Multi-Layered Height Maps
Alexander Maximilian Nilles and Stefan Müller
Neural and Differentiable Rendering
Neural Acquisition & Representation of Subsurface Scattering
Arjun Majumdar, Raphael Braun, and Hendrik Lensch
A Bag of Tricks for Efficient Implicit Neural Point Clouds
Florian Hahlbohm, Linus Franke, Leon Overkämping, Paula Wespe, Susana Castillo, Martin Eisemann, and Marcus Magnor
Learning Neural Antiderivatives
Fizza Rubab, Ntumba Elie Nsampi, Martin Balint, Felix Mujkanovic, Hans-Peter Seidel, Tobias Ritschel, and Thomas Leimkühler
Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data
Tim Elsner, Paula Usinger, Victor Czech, Gregor Kobsik, Yanjiang He, Isaak Lim, and Leif Kobbelt
Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering
Laura Fink, Linus Franke, Bernhard Egger, Joachim Keinert, and Marc Stamminger
Visualization, Visual Analytics, and VR
XMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics
Reyhaneh Sabbagh Gol, Dimitar Valkov, and Lars Linsen
Uncovering Relations in High-Dimensional Behavioral Data of Drosophila Melanogaster
Michael Thane, Kai Michael Blum, and Dirk J. Lehmann
Val-LLM: A Visual Analytics Approach for the Critical Validation of LLM-Generated Tabular Data
Madhav Sachdeva, Christopher Narayanan, Marvin Wiedenkeller, Jana Sedlakova, and Jürgen Bernard
An Investigation of the Apple Vision Pro for Out-of-Core Ray-Guided Volume Rendering with BorgVR
Camilla Hrycak and Jens Krüger
Imaging and Image Processing
Towards Integrating Multi-Spectral Imaging with Gaussian Splatting
Josef Grün, Lukas Meyer, Maximilian Weiherer, Bernhard Egger, Marc Stamminger, and Linus Franke
Fast Camera Calibration from Orthographic Views of Rotated Objects
Arne Rak, Tristan Wirth, Volker Knauthe, Arjan Kuijper, and Dieter W. Fellner
Image Pre-Segmentation from Shadow Masks
Moritz Heep, Amal Dev Parakkat, and Eduard Zell
Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection
Andrei-Timotei Ardelean, Patrick Rückbeil, and Tim Weyrich
CharGen: Fast and Fluent Portrait Modification
Jan-Niklas Dihlmann, Arnela Killguss, and Hendrik Lensch
Geometry, Simulation, and Optimization
Robust Discrete Differential Operators for Wild Geometry
Sven Dominik Wagner and Mario Botsch
Bijective Feature-Aware Contour Matching
Zain Selman, Nils Speetzen, and Leif Kobbelt
Differentiable XPBD for Gradient-Based Learning of Physical Parameters from Motion
Simone Drysch, David Stotko, and Reinhard Klein
Bin-VBSR: Variable Block Size Binned Block-Compressed Sparse Row for Efficient GPU-Accelerated Finite Element Analysis
Florian Pfeil, Stephanie Ferreira, and Johannes Sebastian Mueller-Roemer
Exploring the Geometry of Swarm Intelligence: Negative Inertia and Ellipsoidal Search Space Evolution in PSO
Katharina Krämer, Stefan Müller, and Michael Kosterhon

BibTeX (VMV2025)
@inproceedings{
10.2312:vmv.20252022,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
VMV 2025: Frontmatter}},
author = {
Egger, Bernhard
and
Günther, Tobias
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20252022}
}
@inproceedings{
10.2312:vmv.20251226,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Alias-Free Shadows with Ray Cones for Alpha Tested Geometry}},
author = {
Brüll, Felix
and
Kern, René
and
Grosch, Thorsten
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251226}
}
@inproceedings{
10.2312:vmv.20251227,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Fast Rendering of Large-Scale Dynamic Multi-Layered Height Maps}},
author = {
Nilles, Alexander Maximilian
and
Müller, Stefan
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251227}
}
@inproceedings{
10.2312:vmv.20251228,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Neural Acquisition & Representation of Subsurface Scattering}},
author = {
Majumdar, Arjun
and
Braun, Raphael
and
Lensch, Hendrik
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251228}
}
@inproceedings{
10.2312:vmv.20251229,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
A Bag of Tricks for Efficient Implicit Neural Point Clouds}},
author = {
Hahlbohm, Florian
and
Franke, Linus
and
Overkämping, Leon
and
Wespe, Paula
and
Castillo, Susana
and
Eisemann, Martin
and
Magnor, Marcus
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251229}
}
@inproceedings{
10.2312:vmv.20251230,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Learning Neural Antiderivatives}},
author = {
Rubab, Fizza
and
Nsampi, Ntumba Elie
and
Balint, Martin
and
Mujkanovic, Felix
and
Seidel, Hans-Peter
and
Ritschel, Tobias
and
Leimkühler, Thomas
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251230}
}
@inproceedings{
10.2312:vmv.20251231,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data}},
author = {
Elsner, Tim
and
Usinger, Paula
and
Czech, Victor
and
Kobsik, Gregor
and
He, Yanjiang
and
Lim, Isaak
and
Kobbelt, Leif
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251231}
}
@inproceedings{
10.2312:vmv.20251232,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering}},
author = {
Fink, Laura
and
Franke, Linus
and
Egger, Bernhard
and
Keinert, Joachim
and
Stamminger, Marc
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251232}
}
@inproceedings{
10.2312:vmv.20251233,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
XMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics}},
author = {
Gol, Reyhaneh Sabbagh
and
Valkov, Dimitar
and
Linsen, Lars
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251233}
}
@inproceedings{
10.2312:vmv.20251234,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Uncovering Relations in High-Dimensional Behavioral Data of Drosophila Melanogaster}},
author = {
Thane, Michael
and
Blum, Kai Michael
and
Lehmann, Dirk J.
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251234}
}
@inproceedings{
10.2312:vmv.20251235,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Val-LLM: A Visual Analytics Approach for the Critical Validation of LLM-Generated Tabular Data}},
author = {
Sachdeva, Madhav
and
Narayanan, Christopher
and
Wiedenkeller, Marvin
and
Sedlakova, Jana
and
Bernard, Jürgen
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251235}
}
@inproceedings{
10.2312:vmv.20251236,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
An Investigation of the Apple Vision Pro for Out-of-Core Ray-Guided Volume Rendering with BorgVR}},
author = {
Hrycak, Camilla
and
Krüger, Jens
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251236}
}
@inproceedings{
10.2312:vmv.20251237,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Towards Integrating Multi-Spectral Imaging with Gaussian Splatting}},
author = {
Grün, Josef
and
Meyer, Lukas
and
Weiherer, Maximilian
and
Egger, Bernhard
and
Stamminger, Marc
and
Franke, Linus
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251237}
}
@inproceedings{
10.2312:vmv.20251238,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Fast Camera Calibration from Orthographic Views of Rotated Objects}},
author = {
Rak, Arne
and
Wirth, Tristan
and
Knauthe, Volker
and
Kuijper, Arjan
and
Fellner, Dieter W.
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251238}
}
@inproceedings{
10.2312:vmv.20251239,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Image Pre-Segmentation from Shadow Masks}},
author = {
Heep, Moritz
and
Parakkat, Amal Dev
and
Zell, Eduard
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251239}
}
@inproceedings{
10.2312:vmv.20251240,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection}},
author = {
Ardelean, Andrei-Timotei
and
Rückbeil, Patrick
and
Weyrich, Tim
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251240}
}
@inproceedings{
10.2312:vmv.20251241,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
CharGen: Fast and Fluent Portrait Modification}},
author = {
Dihlmann, Jan-Niklas
and
Killguss, Arnela
and
Lensch, Hendrik
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251241}
}
@inproceedings{
10.2312:vmv.20251242,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Robust Discrete Differential Operators for Wild Geometry}},
author = {
Wagner, Sven Dominik
and
Botsch, Mario
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251242}
}
@inproceedings{
10.2312:vmv.20251243,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Bijective Feature-Aware Contour Matching}},
author = {
Selman, Zain
and
Speetzen, Nils
and
Kobbelt, Leif
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251243}
}
@inproceedings{
10.2312:vmv.20251244,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Differentiable XPBD for Gradient-Based Learning of Physical Parameters from Motion}},
author = {
Drysch, Simone
and
Stotko, David
and
Klein, Reinhard
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251244}
}
@inproceedings{
10.2312:vmv.20251245,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Bin-VBSR: Variable Block Size Binned Block-Compressed Sparse Row for Efficient GPU-Accelerated Finite Element Analysis}},
author = {
Pfeil, Florian
and
Ferreira, Stephanie
and
Mueller-Roemer, Johannes Sebastian
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251245}
}
@inproceedings{
10.2312:vmv.20251246,
booktitle = {
Vision, Modeling, and Visualization},
editor = {
Egger, Bernhard
and
Günther, Tobias
}, title = {{
Exploring the Geometry of Swarm Intelligence: Negative Inertia and Ellipsoidal Search Space Evolution in PSO}},
author = {
Krämer, Katharina
and
Müller, Stefan
and
Kosterhon, Michael
}, year = {
2025},
publisher = {
The Eurographics Association},
ISBN = {978-3-03868-294-3},
DOI = {
10.2312/vmv.20251246}
}

Browse

Recent Submissions

Now showing 1 - 22 of 22
  • Item
    VMV 2025: Frontmatter
    (The Eurographics Association, 2025) Egger, Bernhard; Günther, Tobias; Egger, Bernhard; Günther, Tobias
  • Item
    Alias-Free Shadows with Ray Cones for Alpha Tested Geometry
    (The Eurographics Association, 2025) Brüll, Felix; Kern, René; Grosch, Thorsten; Egger, Bernhard; Günther, Tobias
    We present a method for computing alias-free, smooth shadows for alpha-tested geometry using a single ray per pixel. Without mipmap filtering, hard shadows from alpha-tested geometry appear very noisy under camera motion. Typically, many ray samples are required to soften the shadows and reduce noise. We propose using mipmaps instead, to achieve a fast and temporally stable solution. To determine the appropriate mipmap level, we introduce novel ray cone operations that account for directional and point light sources.
  • Item
    Fast Rendering of Large-Scale Dynamic Multi-Layered Height Maps
    (The Eurographics Association, 2025) Nilles, Alexander Maximilian; Müller, Stefan; Egger, Bernhard; Günther, Tobias
    In this paper, we develop two methods for fast visualization of fully dynamic large-scale multi-layered height maps (MLHMs). MLHMs are a fairly uncommon data structure in computer graphics, which has been used as an efficient representation of 3D terrain, among other applications. Recently, a 3D hydraulic erosion simulation that utilizes this data structure effectively, allowing for real-time simulation of large scale terrain, was developed, but the fast simulation was paired with slow visualization. We extend this previous work with two efficient visualization methods. Rendering the MLHM as boxes is done using ray tracing, while a smooth surface is rendered by ray marching an implicit surface generated by smoothing the MLHM, following previous work. Both techniques are accelerated using a hierarchical data structure built directly from the MLHM, which enables quadtree-like traversal using 2D DDA, where each 2D cell contains 3D axis-aligned bounding boxes (AABBs). This data structure is adapted to accelerate ray marching by appropriately padding AABBs with the smoothing radius. We further propose a soft shadow method and geometric ambient occlusion that work in tandem with this data structure. Our visualization is fast enough to support fully dynamic terrain in real-time, where simulation, creation of our data structure and visualization are done every single frame in real-time for terrain resolutions up to 40962. For lower resolutions, it is possible to run expensive ray tracing and geometric ambient occlusion effects at full window resolution every frame with real-time or interactive frame rates.
  • Item
    Neural Acquisition & Representation of Subsurface Scattering
    (The Eurographics Association, 2025) Majumdar, Arjun; Braun, Raphael; Lensch, Hendrik; Egger, Bernhard; Günther, Tobias
    We present a method to acquire and estimate the sub-surface scattering properties of light transport at a highly detailed level by learning the pixel footprint response at each point on the object surface. The reconstruction leverages 3D scanning techniques as input to a U-Net CNN. A stereo projector-camera setup using phase-shifted profilometry (PSP) patterns efficiently captures the data for a variety of scattering objects. Reconstructing dense pixel footprints allows for relighting with arbitrary high-resolution projector patterns. The final output is a relit color image. Qualitative and quantitative comparison against illuminated realworld captured images demonstrate that the predicted footprints are almost identical to the actual responses. The same model is trained for multiple views across multiple objects such that the learned representations can be used to generalize to unseen sub-surface scattering materials as well.
  • Item
    A Bag of Tricks for Efficient Implicit Neural Point Clouds
    (The Eurographics Association, 2025) Hahlbohm, Florian; Franke, Linus; Overkämping, Leon; Wespe, Paula; Castillo, Susana; Eisemann, Martin; Magnor, Marcus; Egger, Bernhard; Günther, Tobias
    Implicit Neural Point Cloud (INPC) is a recent hybrid representation that combines the expressiveness of neural fields with the efficiency of point-based rendering, achieving state-of-the-art image quality in novel view synthesis. However, as with other high-quality approaches that query neural networks during rendering, the practical usability of INPC is limited by comparatively slow rendering. In this work, we present a collection of optimizations that significantly improve both the training and inference performance of INPC without sacrificing visual fidelity. The most significant modifications are an improved rasterizer implementation, more effective sampling techniques, and the incorporation of pre-training for the convolutional neural network used for hole-filling. Furthermore, we demonstrate that points can be modeled as small Gaussians during inference to further improve quality in extrapolated, e.g., close-up views of the scene. We design our implementations to be broadly applicable beyond INPC and systematically evaluate each modification in a series of experiments. Our optimized INPC pipeline achieves up to 25% faster training, 2× faster rendering, and 20% reduced VRAM usage paired with slight image quality improvements.
  • Item
    Learning Neural Antiderivatives
    (The Eurographics Association, 2025) Rubab, Fizza; Nsampi, Ntumba Elie; Balint, Martin; Mujkanovic, Felix; Seidel, Hans-Peter; Ritschel, Tobias; Leimkühler, Thomas; Egger, Bernhard; Günther, Tobias
    Neural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in continuous neural contexts. We introduce and analyze a range of neural methods for repeated integration, including both adaptations of prior work and novel designs. Our evaluation spans multiple input dimensionalities and integration orders, assessing both reconstruction quality and performance in downstream tasks such as filtering and rendering. These results enable integrating classical cumulative operators into modern neural systems and offer insights into learning tasks involving differential and integral operators.
  • Item
    Quantised Global Autoencoder: A Holistic Approach to Representing Visual Data
    (The Eurographics Association, 2025) Elsner, Tim; Usinger, Paula; Czech, Victor; Kobsik, Gregor; He, Yanjiang; Lim, Isaak; Kobbelt, Leif; Egger, Bernhard; Günther, Tobias
    Quantised autoencoders usually split images into local patches, each encoded by one token. This representation is potentially inefficient, as the same number of tokens are spent per region, regardless of the visual information content in that region. To mitigate uneven distribution of information content, modern architectures provide an adaptive discretisation or add an attention mechanism to the autoencoder to infuse global information into the local tokens. Despite these improvements, tokens are still associated with a local image region. In contrast, our method is inspired by spectral decompositions which transform an input signal into a superposition of global frequencies. Taking the data-driven perspective, we train an encoder that produces a combination of tokens that are then decoded jointly, going beyond the simple linear superposition of spectral decompositions. We achieve this global description with an efficient transpose operation between features and channels and demonstrate how our global and holistic representation improves compression and can boost downstream tasks like generation.
  • Item
    Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering
    (The Eurographics Association, 2025) Fink, Laura; Franke, Linus; Egger, Bernhard; Keinert, Joachim; Stamminger, Marc; Egger, Bernhard; Günther, Tobias
    Accurate depth estimation is at the core of many applications in computer graphics, vision, and robotics. Current state-ofthe- art monocular depth estimators, trained on extensive datasets, generalize well but lack 3D consistency needed for many applications. In this paper, we combine the strength of those generalizing monocular depth estimation techniques with multiview data by framing this as an analysis-by-synthesis optimization problem to lift and refine such relative depth maps to accurate error-free depth maps. After an initial global scale estimation through structure-from-motion point clouds, we further refine the depth map through optimization enforcing multi-view consistency via photometric and geometric losses with differentiable rendering of the meshed depth map. In a two-stage optimization, scaling is further refined first, and afterwards artifacts and errors in the depth map are corrected via nearby-view photometric supervision. Our evaluation shows that our method is able to generate detailed, high-quality, view consistent, accurate depth maps, also in challenging indoor scenarios, and outperforms state-of-the-art multi-view depth reconstruction approaches on such datasets. Project page and source code can be found at https://lorafib.github.io/ref_depth/.
  • Item
    XMTC: Explainable Early Classification of Multivariate Time Series in Reach-to-Grasp Hand Kinematics
    (The Eurographics Association, 2025) Gol, Reyhaneh Sabbagh; Valkov, Dimitar; Linsen, Lars; Egger, Bernhard; Günther, Tobias
    Using multiple hand sensors, hand kinematics can be measured in Human-Computer Interaction (HCI) with the intention to predict the user's intention in a reach-to-grasp action, leading to multivariate time series data. Then, the goal is to classify the multivariate time series data, where the class shall be predicted as early as possible. To investigate the prediction evolution, detect and analyze challenging conditions, and identify the best trade-off between early prediction and prediction quality, we present XMTC. XMTC incorporates visualizations on accuracy over time, multivariate time series classification probabilities, confusion matrices, and partial dependence plots for a trustworthy classification production. We employ XMTC to real-world HCI data in multiple scenarios to achieve good early classifications, as well as insights into which conditions are easy to distinguish, which multivariate time series measurements impose challenges, and which features have the most impact.
  • Item
    Uncovering Relations in High-Dimensional Behavioral Data of Drosophila Melanogaster
    (The Eurographics Association, 2025) Thane, Michael; Blum, Kai Michael; Lehmann, Dirk J.; Egger, Bernhard; Günther, Tobias
    Understanding how behaviour changes under genetic or experimental conditions is a key challenge in behavioural neuroscience. High-throughput tracking enables the collection of high-dimensional datasets describing locomotion, posture, and stimulus orientation in Drosophila melanogaster larvae (fruit fly). However, exploring relations across numerous dimensions remains challenging. We present a Visual Analytics system that integrates coordinated views, type-aware relation metrics, and hierarchical clustering to support relation discovery and validation in behavioural data. The system was initially developed based on prior experience and refined through evaluation with domain experts to address key analysis tasks, including grouping dimensions, exploring behavioural patterns, and validating hypotheses. We demonstrate how it supports both confirmatory and exploratory workflows, enabling users to confirm known effects and uncover novel patterns-such as an unexpected correlation between head-casting behaviour and locomotion speed. This work highlights how tailored visual analysis can advance behavioural research.
  • Item
    Val-LLM: A Visual Analytics Approach for the Critical Validation of LLM-Generated Tabular Data
    (The Eurographics Association, 2025) Sachdeva, Madhav; Narayanan, Christopher; Wiedenkeller, Marvin; Sedlakova, Jana; Bernard, Jürgen; Egger, Bernhard; Günther, Tobias
    Large Language Models (LLMs) are emerging as promising approaches for tabular data generation and enrichment, helping to ease constraints related to data availability. However, the reliable use of LLM-generated data remains challenging, e.g., due to hallucinations and inconsistencies. While some validation approaches exist, five key challenges remain: the lack of explanations and transparency in how values are generated, balancing fine-grained accurate with coarse-grained scalable validation, validating generated data without ground truth, and evaluating plausibility, semantic relevance, and downstream utility. To address these challenges, we present Val-LLM, a novel visual analytics approach for the critical validation of LLM-generated tabular data. Val-LLM enables users to contextualize generated data values with explanations, externalize human expert knowledge, relate LLM outputs with existing data, and assess the data utility in an application downstream. We conducted a user study to evaluate Val-LLM. Results highlight the usefulness of supporting multiple levels of granularity and enabling human knowledge externalization for validation. The study also indicates the need to study validation workflows and workflow flexibility, based on user domain experience and user preferences. Our work supports the trustworthy and effective use of LLM-generated tabular data by integrating visual analytics for systematic data validation.
  • Item
    An Investigation of the Apple Vision Pro for Out-of-Core Ray-Guided Volume Rendering with BorgVR
    (The Eurographics Association, 2025) Hrycak, Camilla; Krüger, Jens; Egger, Bernhard; Günther, Tobias
    We present an in-depth investigation of the Apple Vision Pro as a platform for large-scale volume visualization, focusing on both its technical capabilities and practical limitations in immersive rendering scenarios. Our study centers on BorgVR, a custom-built volume rendering system that implements a bricked, ray-guided, and out-of-core rendering pipeline tailored to the unique characteristics of the Vision Pro and the visionOS graphics stack. BorgVR is designed to overcome memory and performance bottlenecks associated with rendering structured grids that exceed device-local memory. Through dynamic data streaming, hierarchical bricking, GPU-accelerated early ray termination and empty-space skipping, the system achieves interactive frame rates for gigabyte-scale datasets, even under the constraints of mobile spatial computing. We analyze how well the Apple Vision Pro supports such workloads across its distinct rendering modes. Beyond demonstrating system performance, we evaluate the Vision Pro's suitability for scientific visualization-highlighting its strengths in display fidelity and sensor integration, while also documenting friction points such as GPU architecture constraints, memory management, and platform-specific development hurdles. The open-source release of BorgVR provides a reusable foundation for the community, facilitating future research and application development in immersive volume visualization.
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    Towards Integrating Multi-Spectral Imaging with Gaussian Splatting
    (The Eurographics Association, 2025) Grün, Josef; Meyer, Lukas; Weiherer, Maximilian; Egger, Bernhard; Stamminger, Marc; Franke, Linus; Egger, Bernhard; Günther, Tobias
    We present a study of how to integrate color (RGB) and multi-spectral imagery (red, green, red-edge, and near-infrared) into the 3D Gaussian Splatting (3DGS) framework - a state-of-the-art explicit radiance-field-based method for fast and high-fidelity 3D reconstruction from multi-view images [KKLD23]. While 3DGS excels on RGB data, naïve per-band optimization of additional spectra yields poor reconstructions due to inconsistently appearing geometry in the spectral domain. This problem is prominent, even though the actual geometry is the same, regardless of spectral modality. To investigate this, we evaluate three strategies: 1) Separate per-band reconstruction with no shared structure; 2) Splitting optimization, in which we first optimize RGB geometry, copy it, and then fit each new band to the model by optimizing both geometry and band representation; and 3) Joint, in which the modalities are jointly optimized, optionally with an initial RGB-only phase. We showcase through quantitative metrics and qualitative novel-view renderings on multi-spectral datasets the effectiveness of our dedicated optimized Joint strategy, increasing overall spectral reconstruction as well as enhancing RGB results through spectral cross-talk. We therefore suggest integrating multi-spectral data directly into the spherical harmonics color components to compactly model each Gaussian's multi-spectral reflectance. Moreover, our analysis reveals several key trade-offs in when and how to introduce spectral bands during optimization, offering practical insights for robust multi-modal 3DGS reconstruction. The project page and code is located at: meyerls.github.io/towards_multi_spec_splat
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    Fast Camera Calibration from Orthographic Views of Rotated Objects
    (The Eurographics Association, 2025) Rak, Arne; Wirth, Tristan; Knauthe, Volker; Kuijper, Arjan; Fellner, Dieter W.; Egger, Bernhard; Günther, Tobias
    Accurate camera calibration is crucial for high-quality 3D reconstruction in computer vision applications. In industrial measuring scenarios, turntable sequences are often captured using telecentric lenses to overcome the foreshortening effect. While specialized Structure-from-Motion (SfM) solutions exist for orthographic projection, these methods are limited to textured objects. Approaches that leverage the scanned object's silhouette for camera calibration are independent of texture but are often restricted to smooth objects or require non-trivial optimization initializations to converge. In this work, we present a novel silhouette-based approach to estimate the rotation axis of a turntable under orthographic projection, extending the applicability to complex geometries, while requiring little to none parameter adjustments. By identifying the symmetry axis of the object's contour envelope and establishing frontier point correspondences on circular trajectories, we robustly estimate the azimuth and inclination angles of the rotation axis, enabling accurate camera pose computation. We evaluate our approach on synthetic datasets comprising four models with varying characteristics and compare it to a state-of-the-art orthographic SfM method, achieving comparable accuracy, while reducing computational cost 37-fold and eliminating reliance on object texture.
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    Image Pre-Segmentation from Shadow Masks
    (The Eurographics Association, 2025) Heep, Moritz; Parakkat, Amal Dev; Zell, Eduard; Egger, Bernhard; Günther, Tobias
    Image segmentation has gained a lot of attention in the past. When working with photometric stereo data, we discovered that shadow cues provide valuable spatial information, especially when combining multiple images of the same scene under different lighting conditions. In the following, we present a robust method to pre-segment images, relying heavily on shadow masks as the main input. We first detect object contours from light to shadow transitions. In the second step, we run an image segmentation algorithm based on Delaunay triangulation that is capable of closing the gaps between contours. Our method requires spatial input data but is free from training data. Initial results look promising, generating pre-segmentations close to recent data-driven image segmentation algorithms.
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    Quantized FCA: Efficient Zero-Shot Texture Anomaly Detection
    (The Eurographics Association, 2025) Ardelean, Andrei-Timotei; Rückbeil, Patrick; Weyrich, Tim; Egger, Bernhard; Günther, Tobias
    Zero-shot anomaly localization is a rising field in computer vision research, with important progress in recent years. This work focuses on the problem of detecting and localizing anomalies in textures, where anomalies can be defined as the regions that deviate from the overall statistics, violating the stationarity assumption. The main limitation of existing methods is their high running time, making them impractical for deployment in real-world scenarios, such as assembly line monitoring. We propose a real-time method, named QFCA, which implements a quantized version of the feature correspondence analysis (FCA) algorithm. By carefully adapting the patch statistics comparison to work on histograms of quantized values, we obtain a 10× speedup with little to no loss in accuracy. Moreover, we introduce a feature preprocessing step based on principal component analysis, which enhances the contrast between normal and anomalous features, improving the detection precision on complex textures. Our method is thoroughly evaluated against prior art, comparing favorably with existing methods. Project page: reality.tf.fau.de/pub/ardelean2025quantized.html.
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    CharGen: Fast and Fluent Portrait Modification
    (The Eurographics Association, 2025) Dihlmann, Jan-Niklas; Killguss, Arnela; Lensch, Hendrik; Egger, Bernhard; Günther, Tobias
    Interactive editing of character images with diffusion models remains challenging due to the inherent trade-off between fine-grained control, generation speed, and visual fidelity. We introduce CharGen, a character-focused editor that combines attribute-specific Concept Sliders, trained to isolate and manipulate attributes such as facial feature size, expression, and decoration with the StreamDiffusion sampling pipeline for more interactive performance. To counteract the loss of detail that often accompanies accelerated sampling, we propose a lightweight Repair Step that reinstates fine textures without compromising structural consistency. Throughout extensive ablation studies and in comparison to open-source InstructPix2Pix and closedsource Google Gemini, and a comprehensive user study, CharGen achieves two-to-four-fold faster edit turnaround with precise editing control and identity-consistent results. Project page: https://chargen.jdihlmann.com/
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    Robust Discrete Differential Operators for Wild Geometry
    (The Eurographics Association, 2025) Wagner, Sven Dominik; Botsch, Mario; Egger, Bernhard; Günther, Tobias
    Many 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.
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    Bijective Feature-Aware Contour Matching
    (The Eurographics Association, 2025) Selman, Zain; Speetzen, Nils; Kobbelt, Leif; Egger, Bernhard; Günther, Tobias
    Computing maps between data sequences is a fundamental problem with various applications in the fields of geometry and signal processing. As such, a multitude of approaches exist, that make trade-offs between flexibility, performance, and accuracy. Even recent approaches cannot be applied to periodic data, such as contours, without significant compromises due to their map representation or method of optimization. We propose a universal method to optimize maps between periodic and non periodic univariate sequences. By continuously optimizing a piecewise linear approximation of the smooth map on a common intermediate domain, we decouple the map and input resolution. Our optimization offers bijectivity guarantees and flexibility with regards to applications and data modality. To robustly converge towards a high quality solution we initially apply a lowpass filter to the input. This creates a scale space that suppresses local features in the early phase of the optimization (global phase) and gradually adds them back later (local phase). We demonstrate the versatility of our method on various scenarios with different types of sequences, including multi-contour morphing, signature prototypes, symmetry detection, and 3D motioncapture- data alignment.
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    Differentiable XPBD for Gradient-Based Learning of Physical Parameters from Motion
    (The Eurographics Association, 2025) Drysch, Simone; Stotko, David; Klein, Reinhard; Egger, Bernhard; Günther, Tobias
    Accurate cloth simulation is a vital component in computer graphics, virtual reality, and fashion design. Position-Based Dynamics (PBD) and its extension (XPBD) offer robust and efficient methods for simulating deformable objects like cloth. This paper details the evaluation and comparison of cloth simulations based on XPBD, including its ''small steps'' variant and an Energy- Aware (EA) modification. The XPBD variants are evaluated for their physical plausibility and energy conservation to analyze their suitability for inverse problems. Furthermore, we explore the implementation of a differentiable XPBD simulator, enabling the estimation of material properties and external forces. The differentiable simulator is assessed for its capability to estimate parameters in scenarios of increasing complexity. Results indicate that small time steps with single iterations in XPBD offer good energy behavior, while the EA modification exhibits undesired characteristics. The differentiable simulator successfully estimates single parameters but identifies challenges with multi-parameter optimization due to compensatory effects.
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    Bin-VBSR: Variable Block Size Binned Block-Compressed Sparse Row for Efficient GPU-Accelerated Finite Element Analysis
    (The Eurographics Association, 2025) Pfeil, Florian; Ferreira, Stephanie; Mueller-Roemer, Johannes Sebastian; Egger, Bernhard; Günther, Tobias
    We present Binned Variable Block Compressed Sparse Row (Bin-VBSR), a novel GPU-optimized sparse matrix data structure and associated sparse matrix-vector multiplication algorithm for matrices with variable-size dense blocks. This includes a novel approach to handling long rows in the Binned Compressed Sparse Row (Bin-CSR) family of GPU-optimized sparse matrix data structures. We demonstrate speedups of up to 9.9× over Bin-BCSR* and extend its data compression advantages over compressed sparse row (CSR) to variable block size, resulting in an improvement of up to 50%.
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    Exploring the Geometry of Swarm Intelligence: Negative Inertia and Ellipsoidal Search Space Evolution in PSO
    (The Eurographics Association, 2025) Krämer, Katharina; Müller, Stefan; Kosterhon, Michael; Egger, Bernhard; Günther, Tobias
    This paper introduces a geometry-aware method for analyzing swarm behavior in Particle Swarm Optimization (PSO) based on ellipsoidal modeling. Inspired by the n-ball hitting probability, we propose an abstraction of the search space covered by particles over time. Using principal component analysis (PCA), we approximate the particle distribution at each iteration with ellipsoids, enabling a visual and quantitative assessment of how well the swarm explores and concentrates its search effort. We apply this technique to investigate a PSO variant with negative inertia weights, which has shown promising performance in prior empirical analysis. While negative inertia may appear counterintuitive, our ellipsoidal analysis reveals that it introduces oscillatory search dynamics that balance exploration and exploitation more effectively than standard strategies such as constant inertia or linear decreasing inertia. Our experiments include a six-dimensional medical image registration task and an illustrative two-dimensional Rastrigin function, which serves to visually demonstrate how the swarm structure evolves. The proposed analysis framework provides new insight into swarm dynamics and offers a tool for understanding and comparing the behavior of PSO variants beyond conventional performance metrics.