42-Issue 1

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https://diglib.eg.org/handle/10.2312/2633291

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ComBiNet: Visual Query and Comparison of Bipartite Multivariate Dynamic Social Networks
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Pister, A.; Prieur, C.; Fekete, J.‐D.; Hauser, Helwig and Alliez, Pierre
We present ComBiNet, a visualization, query, and comparison system for exploring bipartite multivariate dynamic social networks. Historians and sociologists study social networks constructed from textual sources mentioning events related to people, such as marriage acts, birth certificates and contracts. We model this type of data using bipartite multivariate dynamic networks to maintain a representation faithful to the original sources while not too complex. Relying on this data model, ComBiNet allows exploring networks using both visual and textual queries using the Cypher language, the two being synchronized to specify queries using the most suitable modality; simple queries are easy to express visually and can be refined textually when they become complex. These queries are used for applying topological and attribute‐based selection on the network. Query results are visualized in the context of the whole network and over a geographical map for geolocalized entities. We also present the design of our interaction techniques for querying social networks to visually compare the selections in terms of topology, measures and attribute distributions. We validate the query and comparison systems by showing how they have been used to answer historical questions and by explaining how they have been improved through a usability study conducted with historians.
ComVis‐Sail: Comparative Sailing Performance Visualization for Coaching
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Pieras, M.; Marroquim, R.; Broekens, D.; Eisemann, E.; Vilanova, A.; Hauser, Helwig and Alliez, Pierre
During training sessions, sailors rely on feedback provided by the coaches to reinforce their skills and improve their performance. Nowadays, the incorporation of sensors on the boats enables coaches to potentially provide more informed feedback to the sailors. A common exercise during practice sessions, consists of two boats of the same class, sailing side by side in a straight line with different boat handling techniques. Coaches try to understand which techniques are that make one boat go faster than the other. The analysis of the obtained data from the boats is challenging given its multi‐dimensional, time‐varying and spatial nature. At present, coaches only rely on aggregated statistics reducing the complexity of the data, hereby losing local and temporal information. We describe a new domain characterization and present a visualization design that allows coaches to analyse the data, structuring their analysis and explore the data from different perspectives. A central element of the tool is the glyph design to intuitively represent and aggregate multiple aspects of the sensor data. We have conducted multiple user studies with naive users, sailors and coaches to evaluate the design and potential of the overall tool.
Decision Boundary Visualization for Counterfactual Reasoning
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Sohns, Jan‐Tobias; Garth, Christoph; Leitte, Heike; Hauser, Helwig and Alliez, Pierre
Machine learning algorithms are widely applied to create powerful prediction models. With increasingly complex models, humans' ability to understand the decision function (that maps from a high‐dimensional input space) is quickly exceeded. To explain a model's decisions, black‐box methods have been proposed that provide either non‐linear maps of the global topology of the decision boundary, or samples that allow approximating it locally. The former loses information about distances in input space, while the latter only provides statements about given samples, but lacks a focus on the underlying model for precise ‘What‐If'‐reasoning. In this paper, we integrate both approaches and propose an interactive exploration method using local linear maps of the decision space. We create the maps on high‐dimensional hyperplanes—2D‐slices of the high‐dimensional parameter space—based on statistical and personal feature mutability and guided by feature importance. We complement the proposed workflow with established model inspection techniques to provide orientation and guidance. We demonstrate our approach on real‐world datasets and illustrate that it allows identification of instance‐based decision boundary structures and can answer multi‐dimensional ‘What‐If'‐questions, thereby identifying counterfactual scenarios visually.
Designing Personalized Garments with Body Movement
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Wolff, Katja; Herholz, Philipp; Ziegler, Verena; Link, Frauke; Brügel, Nico; Sorkine‐Hornung, Olga; Hauser, Helwig and Alliez, Pierre
The standardized sizes used in the garment industry do not cover the range of individual differences in body shape for most people, leading to ill‐fitting clothes, high return rates and overproduction. Recent research efforts in both industry and academia, therefore, focus on virtual try‐on and on‐demand fabrication of individually fitting garments. We propose an interactive design tool for creating custom‐fit garments based on 3D body scans of the intended wearer. Our method explicitly incorporates transitions between various body poses to ensure a better fit and freedom of movement. The core of our method focuses on tools to create a 3D garment shape directly on an avatar without an underlying sewing pattern, and on the adjustment of that garment's rest shape while interpolating and moving through the different input poses. We alternate between cloth simulation and rest shape adjustment based on stretch to achieve the final shape of the garment. At any step in the real‐time process, we allow for interactive changes to the garment. Once the garment shape is finalized for production, established techniques can be used to parameterize it into a 2D sewing pattern or transform it into a knitting pattern.
Detail‐Aware Deep Clothing Animations Infused with Multi‐Source Attributes
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Li, T.; Shi, R.; Kanai, T.; Hauser, Helwig and Alliez, Pierre
This paper presents a novel learning‐based clothing deformation method to generate rich and reasonable detailed deformations for garments worn by bodies of various shapes in various animations. In contrast to existing learning‐based methods, which require numerous trained models for different garment topologies or poses and are unable to easily realize rich details, we use a unified framework to produce high fidelity deformations efficiently and easily. Specifically, we first found that the fit between the garment and the body has an important impact on the degree of folds. We then designed an attribute parser to generate detail‐aware encodings and infused them into the graph neural network, therefore enhancing the discrimination of details under diverse attributes. Furthermore, to achieve better convergence and avoid overly smooth deformations, we proposed to reconstruct output to mitigate the complexity of the learning task. Experimental results show that our proposed deformation method achieves better performance over existing methods in terms of generalization ability and quality of details.
Differentiable Depth for Real2Sim Calibration of Soft Body Simulations
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Arnavaz, K.; Nielsen, M. Kragballe; Kry, P. G.; Macklin, M.; Erleben, K.; Hauser, Helwig and Alliez, Pierre
In this work, we present a novel approach for calibrating material model parameters for soft body simulations using real data. We use a fully differentiable pipeline, combining a differentiable soft body simulator and differentiable depth rendering, which permits fast gradient‐based optimizations. Our method requires no data pre‐processing, and minimal experimental set‐up, as we directly minimize the L2‐norm between raw LIDAR scans and rendered simulation states. In essence, we provide the first marker‐free approach for calibrating a soft‐body simulator to match observed real‐world deformations. Our approach is inexpensive as it solely requires a consumer‐level LIDAR sensor compared to acquiring a professional marker‐based motion capture system. We investigate the effects of different material parameterizations and evaluate convergence for parameter optimization in both single and multi‐material scenarios of varying complexity. Finally, we show that our set‐up can be extended to optimize for dynamic behaviour as well.
Editorial
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Alliez, Pierre; Hauser, Helwig; Hauser, Helwig and Alliez, Pierre
Efficient Storage and Importance Sampling for Fluorescent Reflectance
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Hua, Q.; Tázlar, V.; Fichet, A.; Wilkie, A.; Hauser, Helwig and Alliez, Pierre
We propose a technique for efficient storage and importance sampling of fluorescent spectral data. Fluorescence is fully described by a re‐radiation matrix, which for a given input wavelength indicates how much energy is re‐emitted at other wavelengths. However, such representation has a considerable memory footprint. To significantly reduce memory requirements, we propose the use of Gaussian mixture models for the representation of re‐radiation matrices. Instead of the full‐resolution matrix, we work with a set of Gaussian parameters that also allow direct importance sampling. Furthermore, if accuracy is of concern, a re‐radiation matrix can be used jointly with efficient importance sampling provided by the Gaussian mixture. In this paper, we present our pipeline for efficient storage of bispectral data and provide its extensive evaluation on a large set of bispectral measurements. We show that our method is robust and colour accurate even with its comparably minor memory requirements and that it can be seamlessly integrated into a standard Monte Carlo path tracer.
HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniques
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Chatzimparmpas, A.; Paulovich, F. V.; Kerren, A.; Hauser, Helwig and Alliez, Pierre
Despite the tremendous advances in machine learning (ML), training with imbalanced data still poses challenges in many real‐world applications. Among a series of diverse techniques to solve this problem, sampling algorithms are regarded as an efficient solution. However, the problem is more fundamental, with many works emphasizing the importance of instance hardness. This issue refers to the significance of managing unsafe or potentially noisy instances that are more likely to be misclassified and serve as the root cause of poor classification performance.This paper introduces HardVis, a visual analytics system designed to handle instance hardness mainly in imbalanced classification scenarios. Our proposed system assists users in visually comparing different distributions of data types, selecting types of instances based on local characteristics that will later be affected by the active sampling method, and validating which suggestions from undersampling or oversampling techniques are beneficial for the ML model. Additionally, rather than uniformly undersampling/oversampling a specific class, we allow users to find and sample easy and difficult to classify training instances from all classes. Users can explore subsets of data from different perspectives to decide all those parameters, while HardVis keeps track of their steps and evaluates the model's predictive performance in a test set separately. The end result is a well‐balanced data set that boosts the predictive power of the ML model. The efficacy and effectiveness of HardVis are demonstrated with a hypothetical usage scenario and a use case. Finally, we also look at how useful our system is based on feedback we received from ML experts.
HDRNet: High‐Dimensional Regression Network for Point Cloud Registration
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Gao, Jian; Zhang, Yuhe; Liu, Zehua; Li, Siyi; Hauser, Helwig and Alliez, Pierre
Abstract‐3D point cloud registration is a crucial topic in the reverse engineering, computer vision and robotics fields. The core of this problem is to estimate a transformation matrix for aligning the source point cloud with a target point cloud. Several learning‐based methods have achieved a high performance. However, they are challenged with both partial overlap point clouds and multiscale point clouds, since they use the singular value decomposition (SVD) to find the rotation matrix without fully considering the scale information. Furthermore, previous networks cannot effectively handle the point clouds having large initial rotation angles, which is a common practical case. To address these problems, this paper presents a learning‐based point cloud registration network, namely HDRNet, which consists of four stages: local feature extraction, correspondence matrix estimation, feature embedding and fusion and parametric regression. HDRNet is robust to noise and large rotation angles, and can effectively handle the partial overlap and multi‐scale point clouds registration. The proposed model is trained on the ModelNet40 dataset, and compared with ICP, SICP, FGR and recent learning‐based methods (PCRNet, IDAM, RGMNet and GMCNet) under several settings, including its performance on moving to invisible objects, with higher success rates. To verify the effectiveness and generality of our model, we also further tested our model on the Stanford 3D scanning repository.
Improved Evaluation and Generation Of Grid Layouts Using Distance Preservation Quality and Linear Assignment Sorting
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Barthel, K. U.; Hezel, N.; Jung, K.; Schall, K.; Hauser, Helwig and Alliez, Pierre
Images sorted by similarity enables more images to be viewed simultaneously, and can be very useful for stock photo agencies or e‐commerce applications. Visually sorted grid layouts attempt to arrange images so that their proximity on the grid corresponds as closely as possible to their similarity. Various metrics exist for evaluating such arrangements, but there is low experimental evidence on correlation between human perceived quality and metric value. We propose distance preservation quality (DPQ) as a new metric to evaluate the quality of an arrangement. Extensive user testing revealed stronger correlation of DPQ with user‐perceived quality and performance in image retrieval tasks compared to other metrics. In addition, we introduce Fast linear assignment sorting (FLAS) as a new algorithm for creating visually sorted grid layouts. FLAS achieves very good sorting qualities while improving run time and computational resources.
Investigation and Simulation of Diffraction on Rough Surfaces
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Clausen, O.; Chen, Y.; Fuhrmann, A.; Marroquim, R.; Hauser, Helwig and Alliez, Pierre
Simulating light–matter interaction is a fundamental problem in computer graphics. A particular challenge is the simulation of light interaction with rough surfaces due to diffraction and multiple scattering phenomena. To properly model these phenomena, wave‐optics have to be considered. Nevertheless, the most accurate BRDF models, including wave‐optics, are computationally expensive, and the resulting renderings have not been systematically compared to real‐world measurements. This work sheds more light on reflectance variations due to surface roughness. More specifically, we look at wavelength shifts that lead to reddish and blueish appearances. These wavelength shifts have been scarcely reported in the literature, and, in this paper, we provide the first thorough analysis from precise measured data. We measured the spectral in‐plane BRDF of aluminium samples with varying roughness and further acquired the surface topography with a confocal microscope. The measurements show that the rough samples have, on average, a reddish and blueish appearance in the forward and back‐scattering, respectively. Our investigations conclude that this is a diffraction‐based effect that dominates the overall appearance of the samples. Simulations using a virtual gonioreflectometer further confirm our claims. We propose a linear model that can closely fit such phenomena, where the slope of the wavelength shifts depends on the incident and reflection direction. Based on these insights, we developed a simple BRDF model based on the Cook–Torrance model that considers such wavelength shifts.
Issue Information
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Hauser, Helwig and Alliez, Pierre
Mesh Draping: Parametrization‐Free Neural Mesh Transfer
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Hertz, A.; Perel, O.; Giryes, R.; Sorkine‐Hornung, O.; Cohen‐Or, D.; Hauser, Helwig and Alliez, Pierre
Despite recent advances in geometric modelling, 3D mesh modelling still involves a considerable amount of manual labour by experts. In this paper, we introduce Mesh Draping: a neural method for transferring existing mesh structure from one shape to another. The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh. At its core, our method deforms the source mesh using progressive positional encoding (PE). We show that by leveraging gradually increasing frequencies to guide the neural optimization, we are able to achieve stable and high‐quality mesh transfer. Our approach is simple and requires little user guidance, compared to contemporary surface mapping techniques which rely on parametrization or careful manual tuning. Most importantly, Mesh Draping is a parameterization‐free method, and thus applicable to a variety of target shape representations, including point clouds, polygon soups and non‐manifold meshes. We demonstrate that the transferred meshing remains faithful to the source mesh design characteristics, and at the same time fits the target geometry well.
Model Averaging and Bootstrap Consensus‐based Uncertainty Reduction in Diffusion MRI Tractography
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Gruen, J.; van der Voort, G.; Schultz, T.; Hauser, Helwig and Alliez, Pierre
Diffusion magnetic resonance imaging (dMRI) tractography has the unique ability to reconstruct major white matter tracts non‐invasively and is, therefore, widely used in neurosurgical planning and neuroscience. In this work, we reduce two sources of uncertainty within the tractography pipeline. The first one is the model uncertainty that arises in crossing fibre tractography, from having to estimate the number of relevant fibre compartments in each voxel. We propose a mathematical framework to estimate model uncertainty, and we reduce this type of uncertainty with a model averaging approach that combines the fibre direction estimates from all candidate models, weighted by the posterior probability of the respective model. The second source of uncertainty is measurement noise. We use bootstrapping to estimate this data uncertainty, and consolidate the fibre direction estimates from all bootstraps into a consensus model. We observe that, in most voxels, a traditional model selection strategy selects different models across bootstraps. In this sense, the bootstrap consensus also reduces model uncertainty. Either approach significantly increases the accuracy of crossing fibre tractography in multiple subjects, and combining them provides an additional benefit. However, model averaging is much more efficient computationally.
Monolithic Friction and Contact Handling for Rigid Bodies and Fluids Using SPH
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Probst, T.; Teschner, M.; Hauser, Helwig and Alliez, Pierre
We propose a novel monolithic pure SPH formulation to simulate fluids strongly coupled with rigid bodies. This includes fluid incompressibility, fluid–rigid interface handling and rigid–rigid contact handling with a viable implicit particle‐based dry friction formulation. The resulting global system is solved using a new accelerated solver implementation that outperforms existing fluid and coupled rigid–fluid simulation approaches. We compare results of our simulation method to analytical solutions, show performance evaluations of our solver and present a variety of new and challenging simulation scenarios.
Novel View Synthesis Of Transparent Object From a Single Image
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Zhou, Shizhe; Wang, Zezu; Ye, Dongwei; Hauser, Helwig and Alliez, Pierre
We propose a method for converting a single image of a transparent object into multi‐view photo that enables users observing the object from multiple new angles, without inputting any 3D shape. The complex light paths formed by refraction and reflection makes it challenging to compute the lighting effects of transparent objects from a new angle. We construct an encoder–decoder network for normal reconstruction and texture extraction, which enables synthesizing novel views of transparent object from a set of new views and new environment maps using only one RGB image. By simultaneously considering the optical transmission and perspective variation, our network learns the characteristics of optical transmission and the change of perspective as guidance to the conversion from RGB colours to surface normals. A texture extraction subnetwork is proposed to alleviate the contour loss phenomenon during normal map generation. We test our method using 3D objects within and without our training data, including real 3D objects that exists in our lab, and completely new environment maps that we take using our phones. The results show that our method performs better on view synthesis of transparent objects in complex scenes using only a single‐view image.
PDViz: A Visual Analytics Approach for State Policy Data
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Han, Dongyun; Nayeem, Abdullah‐Al‐Raihan; Windett, Jason; Cho, Isaac; Hauser, Helwig and Alliez, Pierre
Sub‐national governments across the United States implement a variety of policies to address large societal problems and needs. Many policies are picked up or adopted in other states. This process is called policy diffusion and allows researchers to analyse and compare the social, political, and contextual characteristics that lead to adopting certain policies, as well as the efficacy of these policies once adopted. In this paper, we introduce PDViz, a visual analytics approach that allows social scientists to dynamically analyse the policy diffusion history and underlying patterns. It is designed for analysing and answering a list of research questions and tasks posed by social scientists in prior work. To evaluate our system, we present two usage scenarios and conduct interviews with domain experts in political science. The interviews highlight that PDViz provides the result of policy diffusion patterns that align with their domain knowledge as well as the potential to be a learning tool for students to understand the concept of policy diffusion.
Priority‐based encoding of triangle mesh connectivity for a known geometry
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Dvořák, Jan; Káčereková, Zuzana; Vaněček, Petr; Váša, Libor; Hauser, Helwig and Alliez, Pierre
In certain practical situations, the connectivity of a triangle mesh needs to be transmitted or stored given a fixed set of 3D vertices that is known at both ends of the transaction (encoder/decoder). This task is different from a typical mesh compression scenario, in which the connectivity and geometry (vertex positions) are encoded either simultaneously or in reversed order (connectivity first), usually exploiting the freedom in vertex/triangle re‐indexation. Previously proposed algorithms for encoding the connectivity for a known geometry were based on a canonical mesh traversal and predicting which vertex is to be connected to the part of the mesh that is already processed. In this paper, we take this scheme a step further by replacing the fixed traversal with a priority queue of open expansion gates, out of which in each step a gate is selected that has the most certain prediction, that is one in which there is a candidate vertex that exhibits the largest advantage in comparison with other possible candidates, according to a carefully designed quality metric. Numerical experiments demonstrate that this improvement leads to a substantial reduction in the required data rate in comparison with the state of the art.
Proliferating cell nuclear antigen sliding along DNA
(Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Hauser, Helwig and Alliez, Pierre

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