40-Issue 1

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https://diglib.eg.org/handle/10.2312/2633015

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Life cycle of SARS‐CoV‐2: from sketch to visualization in atomistic resolution
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig
Functionality‐Driven Musculature Retargeting
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Ryu, Hoseok; Kim, Minseok; Lee, Seungwhan; Park, Moon Seok; Lee, Kyoungmin; Lee, Jehee; Benes, Bedrich and Hauser, Helwig
We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation‐ready, so we can physically simulate muscle‐actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi‐planar X‐ray images and medical examination.
Temporally Dense Exploration of Moving and Deforming Shapes
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Frey, S.; Benes, Bedrich and Hauser, Helwig
We present our approach for the dense visualization and temporal exploration of moving and deforming shapes from scientific experiments and simulations. Our image space representation is created by convolving a noise texture along shape contours (akin to LIC). Beyond indicating spatial structure via luminosity, we additionally use colour to depict time or classes of shapes via automatically customized maps. This representation summarizes temporal evolution, and provides the basis for interactive user navigation in the spatial and temporal domain in combination with traditional renderings. Our efficient implementation supports the quick and progressive generation of our representation in parallel as well as adaptive temporal splits to reduce overlap. We discuss and demonstrate the utility of our approach using 2D and 3D scalar fields from experiments and simulations.
Framework for Capturing and Editing of Anisotropic Effect Coatings
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Filip, J.; Vávra, R.; Maile, F. J.; Kolafová, M.; Benes, Bedrich and Hauser, Helwig
Coatings are used today for products, ranging from automotive production to electronics and everyday use items. Product design is taking on an increasingly important role, where effect pigments come to the fore, offering a coated surface extra optical characteristics. Individual effect pigments have strong anisotropic, azimuthaly‐dependent behaviour, typically suppressed by a coating application process, randomly orienting pigment particles resulting in isotropic appearance. One exception is a pigment that allows control of the azimuthal orientation of flakes using a magnetic field. We investigate visual texture effects due to such an orientation in a framework allowing efficient capturing, modelling and editing of its appearance. We captured spatially‐varying BRDFs of four coatings containing magnetic effect pigments. As per‐pixel non‐linear fitting cannot preserve coating sparkle effects, we suggest a novel method of anisotropy modelling based on images shifting in an angular domain. The model can be utilized for a fast transfer of desired anisotropy to any isotropic effect coating, while preserving important spatially‐varying visual features of the original coating. The anisotropic behaviour was fitted by a parametric model allowing for editing of coating appearance. This framework allows exploration of anisotropic effect coatings and their appearance transfer to standard effect coatings in a virtual environment.
SketchZooms: Deep Multi‐view Descriptors for Matching Line Drawings
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Navarro, Pablo; Orlando, J. Ignacio; Delrieux, Claudio; Iarussi, Emmanuel; Benes, Bedrich and Hauser, Helwig
Finding point‐wise correspondences between images is a long‐standing problem in image analysis. This becomes particularly challenging for sketch images, due to the varying nature of human drawing style, projection distortions and viewport changes. In this paper, we present the first attempt to obtain a learned descriptor for dense registration in line drawings. Based on recent deep learning techniques for corresponding photographs, we designed descriptors to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape, form, and projection angle. To this end, we have specifically crafted a data set of synthetic sketches using non‐photorealistic rendering over a large collection of part‐based registered 3D models. After training, a neural network generates descriptors for every pixel in an input image, which are shown togeneralize correctly in unseen sketches hand‐drawn by humans. We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches. Code, data and further resources will be publicly released by the time of publication.
TopoAct: Visually Exploring the Shape of Activations in Deep Learning
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Rathore, Archit; Chalapathi, Nithin; Palande, Sourabh; Wang, Bei; Benes, Bedrich and Hauser, Helwig
Deep neural networks such as GoogLeNet, ResNet, and BERT have achieved impressive performance in tasks such as image and text classification. To understand how such performance is achieved, we probe a trained deep neural network by studying neuron activations, i.e.combinations of neuron firings, at various layers of the network in response to a particular input. With a large number of inputs, we aim to obtain a global view of what neurons detect by studying their activations. In particular, we develop visualizations that show the shape of the activation space, the organizational principle behind neuron activations, and the relationships of these activations within a layer. Applying tools from topological data analysis, we present , a visual exploration system to study topological summaries of activation vectors. We present exploration scenarios using that provide valuable insights into learned representations of neural networks. We expect to give a topological perspective that enriches the current toolbox of neural network analysis, and to provide a basis for network architecture diagnosis and data anomaly detection.
A Curvature and Density‐based Generative Representation of Shapes
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Ye, Z.; Umetani, N.; Igarashi, T.; Hoffmann, T.; Benes, Bedrich and Hauser, Helwig
This paper introduces a generative model for 3D surfaces based on a representation of shapes with mean curvature and metric, which are invariant under rigid transformation. Hence, compared with existing 3D machine learning frameworks, our model substantially reduces the influence of translation and rotation. In addition, the local structure of shapes will be more precisely captured, since the curvature is explicitly encoded in our model. Specifically, every surface is first conformally mapped to a canonical domain, such as a unit disk or a unit sphere. Then, it is represented by two functions: the mean curvature half‐density and the vertex density, over this canonical domain. Assuming that input shapes follow a certain distribution in a latent space, we use the variational autoencoder to learn the latent space representation. After the learning, we can generate variations of shapes by randomly sampling the distribution in the latent space. Surfaces with triangular meshes can be reconstructed from the generated data by applying isotropic remeshing and spin transformation, which is given by Dirac equation. We demonstrate the effectiveness of our model on datasets of man‐made and biological shapes and compare the results with other methods.
Towards Light‐Weight Portrait Matting via Parameter Sharing
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Dai, Yutong; Lu, Hao; Shen, Chunhua; Benes, Bedrich and Hauser, Helwig
Traditional portrait matting methods typically consist of a trimap estimation network and a matting network. Here, we propose a new light‐weight portrait matting approach, termed parameter‐sharing portrait matting (PSPM). Different from conventional portrait matting models where the encoder and decoder networks in two tasks are often separately designed, here a single encoder is employed for the two tasks in PSPM, while each task still has its task‐specific decoder. Thus, the role of the encoder is to extract semantic features and two decoders function as a bridge between low‐resolution feature maps generated by the encoder and high‐resolution feature maps for pixel‐wise classification/regression. In particular, three variants capable of implementing the parameter‐sharing portrait matting network are proposed and investigated, respectively. As demonstrated in our experiments, model capacity and computation costs can be reduced significantly, by up to and , respectively, with PSPM, whereas the matting accuracy only slightly deteriorates. In addition, qualitative and quantitative evaluations show that sharing the encoder is an effective way to achieve portrait matting with limited computational budgets, indicating a promising direction for applications of real‐time portrait matting on mobile devices.
Issue Information
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benes, Bedrich and Hauser, Helwig
Stochastic Volume Rendering of Multi‐Phase SPH Data
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Piochowiak, M.; Rapp, T.; Dachsbacher, C.; Benes, Bedrich and Hauser, Helwig
In this paper, we present a novel method for the direct volume rendering of large smoothed‐particle hydrodynamics (SPH) simulation data without transforming the unstructured data to an intermediate representation. By directly visualizing the unstructured particle data, we avoid long preprocessing times and large storage requirements. This enables the visualization of large, time‐dependent, and multivariate data both as a post‐process and in situ. To address the computational complexity, we introduce stochastic volume rendering that considers only a subset of particles at each step during ray marching. The sample probabilities for selecting this subset at each step are thereby determined both in a view‐dependent manner and based on the spatial complexity of the data. Our stochastic volume rendering enables us to scale continuously from a fast, interactive preview to a more accurate volume rendering at higher cost. Lastly, we discuss the visualization of free‐surface and multi‐phase flows by including a multi‐material model with volumetric and surface shading into the stochastic volume rendering.
A Modified Double Gyre with Ground Truth Hyperbolic Trajectories for Flow Visualization
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Wolligandt, S.; Wilde, T.; Rössl, C.; Theisel, H.; Benes, Bedrich and Hauser, Helwig
The model of a flow by Shadden et al. is a standard benchmark data set for the computation of hyperbolic Lagrangian Coherent Structures (LCS) in flow data. While structurally extremely simple, it generates hyperbolic LCS of arbitrary complexity. Unfortunately, the does not come with a well‐defined ground truth: the location of hyperbolic LCS boundaries can only be approximated by numerical methods that usually involve the gradient of the flow map. We present a new benchmark data set that is a small but carefully designed modification of the , which comes with ground truth closed‐form hyperbolic trajectories. This allows for computing hyperbolic LCS boundaries by a simple particle integration without the consideration of the flow map gradient. We use these hyperbolic LCS as a ground truth solution for testing an existing numerical approach for extracting hyperbolic trajectories. In addition, we are able to construct hyperbolic LCS curves that are significantly longer than in existing numerical methods.
Turbulent Details Simulation for SPH Fluids via Vorticity Refinement
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Liu, Sinuo; Wang, Xiaokun; Ban, Xiaojuan; Xu, Yanrui; Zhou, Jing; Kosinka, Jiří; Telea, Alexandru C.; Benes, Bedrich and Hauser, Helwig
A major issue in smoothed particle hydrodynamics (SPH) approaches is the numerical dissipation during the projection process, especially under coarse discretizations. High‐frequency details, such as turbulence and vortices, are smoothed out, leading to unrealistic results. To address this issue, we introduce a vorticity refinement (VR) solver for SPH fluids with negligible computational overhead. In this method, the numerical dissipation of the vorticity field is recovered by the difference between the theoretical and the actual vorticity, so as to enhance turbulence details. Instead of solving the Biot‐Savart integrals, a stream function, which is easier and more efficient to solve, is used to relate the vorticity field to the velocity field. We obtain turbulence effects of different intensity levels by changing an adjustable parameter. Since the vorticity field is enhanced according to the curl field, our method can not only amplify existing vortices, but also capture additional turbulence. Our VR solver is straightforward to implement and can be easily integrated into existing SPH methods.
Optimizing LBVH‐Construction and Hierarchy‐Traversal to accelerate kNN Queries on Point Clouds using the GPU
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Jakob, J.; Guthe, M.; Benes, Bedrich and Hauser, Helwig
Processing point clouds often requires information about the point neighbourhood in order to extract, calculate and determine characteristics. We continue the tradition of developing increasingly faster neighbourhood query algorithms and present a highly efficient algorithm for solving the exact neighbourhood problem in point clouds using the GPU. Both, the required data structures and the NN query, are calculated entirely on the GPU. This enables real‐time performance for large queries in extremely large point clouds. Our experiments show a more than threefold acceleration, compared to state‐of‐the‐art GPU based methods including all memory transfers. In terms of pure query performance, we achieve over answered neighbourhood queries per millisecond for 16 nearest neighbours on common graphics hardware.
Modelling Material Microstructure Using the Perlin Noise Function
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Conde‐Rodríguez, F.; García‐Fernández, Á‐.L.; Torres, J.C.; Benes, Bedrich and Hauser, Helwig
This paper introduces a precise and easy to use method for defining the microstructure of heterogeneous solids. This method is based on the concept of Heterogeneous Composite Bézier Hyperpatch, and allows to accurately represent the primary material proportions, as well as the size and shape of the material phases. The solid microstructure is modelled using two functions: a material distribution function (to compute the portion of the solid volume occupied by each primary material), and a modified Perlin noise function that determines the shape and size of each primary material phase.With this method, the position and orientation of the solid in the modeling space does not affect the portion of its volume that is occupied by each primary material, nor the shape and size of the phases. However, the solid microstructure is coherently and automatically modified when the shape of the solid is edited. Regarding continuity, this method allows to define to which extent continuity (both in shape and material distribution) has to be preserved at the junction of the cells that compose the solid. This makes modeling geometrically complex figures very easy.
Path‐based Monte Carlo Denoising Using a Three‐Scale Neural Network
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Lin, Weiheng; Wang, Beibei; Yang, Jian; Wang, Lu; Yan, Ling‐Qi; Benes, Bedrich and Hauser, Helwig
Monte Carlo rendering is widely used in the movie industry. Since it is costly to produce noise‐free results directly, Monte Carlo denoising is often applied as a post‐process. Recently, deep learning methods have been successfully leveraged in Monte Carlo denoising. They are able to produce high quality denoised results, even with very low sample rate, e.g. 4 spp (sample per pixel). However, for difficult scene configurations, some details could be blurred in the denoised results. In this paper, we aim at preserving more details from inputs rendered with low spp. We propose a novel denoising pipeline that handles three‐scale features ‐ pixel, sample and path ‐ to preserve sharp details, uses an improved Res2Net feature extractor to reduce the network parameters and a smooth feature attention mechanism to remove low‐frequency splotches. As a result, our method achieves higher denoising quality and preserves better details than the previous methods.
ECHO: Extended Convolution Histogram of Orientations for Local Surface Description
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Mitchel, Thomas W.; Rusinkiewicz, Szymon; Chirikjian, Gregory S.; Kazhdan, Michael; Benes, Bedrich and Hauser, Helwig
This paper presents a novel, highly distinctive and robust local surface feature descriptor. Our descriptor is predicated on a simple observation: instead of describing the points in the vicinity of a feature point relative to a reference frame at the feature point, all points in the region describe the feature point relative to their own frames. Isometry invariance is a byproduct of this construction. Our descriptor is derived relative to the extended convolution – a generalization of the standard convolution that allows the filter to adaptively transform as it passes over the domain. As such, we name our descriptor the Extended Convolution Histogram of Orientations (ECHO). It exhibits superior performance compared to popular surface descriptors in both feature matching and shape correspondence experiments. In particular, the ECHO descriptor is highly stable under near‐isometric deformations and remains distinctive under significant levels of noise, tessellation, complex deformations and the kinds of interference commonly found in real data.
Editorial
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Hauser, Helwig; Benes, Bedrich; Benes, Bedrich and Hauser, Helwig
Physics‐based Pathline Clustering and Exploration
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Nguyen, Duong B.; Zhang, Lei; Laramee, Robert S.; Thompson, David; Monico, Rodolfo Ostilla; Chen, Guoning; Benes, Bedrich and Hauser, Helwig
Most existing unsteady flow visualization techniques concentrate on the depiction of geometric patterns in flow, assuming the geometry information provides sufficient representation of the underlying physical characteristics, which is not always the case. To address this challenge, this work proposes to analyse the time‐dependent characteristics of the physical attributes measured along pathlines which can be represented as a series of time activity curves (TAC). We demonstrate that the temporal trends of these TACs can convey the relation between pathlines and certain well‐known flow features (e.g. vortices and shearing layers), which enables us to select pathlines that can effectively represent the physical characteristics of interest and their temporal behaviour in the unsteady flow. Inspired by this observation, a new TAC‐based unsteady flow visualization and analysis framework is proposed. The centre of this framework is a new similarity measure that compares the similarity of two TACs, from which a new spatio‐temporal, hierarchical clustering that classifies pathlines based on their physical attributes, and a TAC‐based pathline exploration and selection strategy are proposed. A visual analytic system incorporating the TAC‐based pathline clustering and exploration is developed, which also provides new visualizations to support the user exploration of unsteady flow using TACs. This visual analytic system is applied to a number of unsteady flow in 2D and 3D to demonstrate its utility. The new system successfully reveals the detailed structure of vortices, the relation between shear layer and vortex formation, and vortex breakdown, which are difficult to convey with conventional methods.
Structural Analogy from a Single Image Pair
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Benaim, S.; Mokady, R.; Bermano, A.; Wolf, L.; Benes, Bedrich and Hauser, Helwig
The task of unsupervised image‐to‐image translation has seen substantial advancements in recent years through the use of deep neural networks. Typically, the proposed solutions learn the characterizing distribution of two large, unpaired collections of images, and are able to alter the appearance of a given image, while keeping its geometry intact. In this paper, we explore the capabilities of neural networks to understand image given only a single pair of images, and . We seek to generate images that are : that is, to generate an image that keeps the appearance and style of , but has a structural arrangement that corresponds to . The key idea is to map between image patches at different scales. This enables controlling the granularity at which analogies are produced, which determines the conceptual distinction between style and content. In addition to , our method can be used to generate high quality imagery in other conditional generation tasks utilizing images and only: guided image synthesis, style and texture transfer, text translation as well as video translation. Our code and additional results are available in
The State of the Art of Spatial Interfaces for 3D Visualization
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Besançon, Lonni; Ynnerman, Anders; Keefe, Daniel F.; Yu, Lingyun; Isenberg, Tobias; Benes, Bedrich and Hauser, Helwig
We survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under‐explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction.

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