41-Issue 6

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https://diglib.eg.org/handle/10.2312/2633218

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TopoNet: Topology Learning for 3D Reconstruction of Objects of Arbitrary Genus
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Ben Charrada, Tarek; Tabia, Hedi; Chetouani, Aladine; Laga, Hamid; Hauser, Helwig and Alliez, Pierre
We propose a deep reinforcement learning‐based solution for the 3D reconstruction of objects of complex topologies from a single RGB image. We use a template‐based approach. However, unlike previous template‐based methods, which are limited to the reconstruction of 3D objects of fixed topology, our approach learns simultaneously the geometry and topology of the target 3D shape in the input image. To this end, we propose a neural network that learns to deform a template to fit the geometry of the target object. Our key contribution is a novel reinforcement learning framework that enables the network to also learn how to adjust, using pruning operations, the topology of the template to best fit the topology of the target object. We train the network in a supervised manner using a loss function that enforces smoothness and penalizes long edges in order to ensure high visual plausibility of the reconstructed 3D meshes. We evaluate the proposed approach on standard benchmarks such as ShapeNet, and in‐the‐wild using unseen real‐world images. We show that the proposed approach outperforms the state‐of‐the‐art in terms of the visual quality of the reconstructed 3D meshes, and also generalizes well to out‐of‐category images.
Wide Gamut Moment‐based Constrained Spectral Uplifting
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Tódová, L.; Wilkie, A.; Fascione, L.; Hauser, Helwig and Alliez, Pierre
Spectral rendering is increasingly used in appearance‐critical rendering workflows due to its ability to predict colour values under varying illuminants. However, directly modelling assets via input of spectral data is a tedious process: and if asset appearance is defined via artist‐created textures, these are drawn in colour space, i.e. RGB. Converting these RGB values to equivalent spectral representations is an ambiguous problem, for which robust techniques have been proposed only comparatively recently. However, other than the resulting RGB values matching under the illuminant the RGB space is defined for (usually D65), these uplifting techniques do not provide the user with further control over the resulting spectral shape. In a recent publication, we have proposed a method for constraining the spectral uplifting process so that for a finite number of input spectra that need to be preserved, it always yields the correct uplifted spectrum for the corresponding RGB value. We extend this previous work, which supported the sRGB gamut only, by describing a method that is able to constrain any spectrum from within the gamut of realisable reflectances. Due to constraints placed on the uplifting process, target RGB values that are in close proximity to one another uplift to spectra within the same metameric family, so that textures with colour variations can be meaningfully uplifted. Renderings uplifted via our method show minimal discrepancies when compared to the original objects.
Real‐Time FE Simulation for Large‐Scale Problems Using Precondition‐Based Contact Resolution and Isolated DOFs Constraints
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Zeng, Z.; Cotin, S.; Courtecuisse, H.; Hauser, Helwig and Alliez, Pierre
This paper presents a fast method to compute large‐scale problems in real‐time finite element simulations in the presence of contact and friction. The approach uses a precondition‐based contact resolution that performs a Cholesky decomposition at low frequency. On exploiting the sparsity in assembled matrices, we propose a reduced and parallel computation scheme to address the expensive computation of the Schur‐complement arisen by detailed mesh and accurate contact response. An efficient GPU‐based solver is developed to parallelise the computation, making it possible to provide real‐time simulations in the presence of coupled constraints for contact and friction response. In addition, the pre‐conditioner is updated at low frequency, implying reuse of the factorised system. To benefit a further speedup, we propose a strategy to share the resolution information between consecutive time steps. We evaluate the performance of our method in different contact applications and compare it with typical approaches on CPU and GPU.
Quad‐fisheye Image Stitching for Monoscopic Panorama Reconstruction
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Cheng, Haojie; Xu, Chunxiao; Wang, Jiajun; Zhao, Lingxiao; Hauser, Helwig and Alliez, Pierre
Monoscopic panorama provides the display of omnidirectional contents surrounding the viewer. An increasingly popular way to reconstruct a panorama is to stitch a collection of fisheye images. However, such non‐planar views may result in problems such as distortions and boundary irregularities. In most cases, the computational expense for stitching non‐planar images is also too high to satisfy real‐time applications. In this paper, a novel monoscopic panorama reconstruction pipeline that produces better quad‐fisheye image stitching results for omnidirectional environment viewing is proposed. The main idea is to apply mesh deformation for image alignment. To optimize inter‐lens parallaxes, unwarped images are firstly cropped and reshuffled to facilitate the circular environment scene composition by the seamless ring‐connection of the panorama borders. Several mesh constraints are then adopted to ensure a high alignment accuracy. After alignment, the boundary of the result is rectified to be rectangular to prevent gapping artefacts. We further extend our approach to video stitching. The temporal smoothness model is added to prevent unexpected artefacts in the panoramic videos. To support interactive applications, our stitching algorithm is programmed using CUDA. The camera motion and average gradient per video frame are further calculated to accelerate for synchronous real‐life panoramic scene reconstruction and visualization. Experimental results demonstrate that our method has advantages in respects of alignment accuracy, adaptability and image quality of the stitching result.
Erratum: Evaluating Data‐type Heterogeneity in Interactive Visual Analyses with Parallel Axes
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Hauser, Helwig and Alliez, Pierre
Narrow‐Band Screen‐Space Fluid Rendering
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Oliveira, Felipe; Paiva, Afonso; Hauser, Helwig and Alliez, Pierre
This paper presents a novel and practical screen‐space liquid rendering for particle‐based fluids for real‐time applications. Our rendering pipeline performs particle filtering only in a narrow‐band around the boundary particles to provide a smooth liquid surface with volumetric rendering effects. We also introduce a novel boundary detection method allowing the user to select particle layers from the liquid interface. The proposed approach is simple, fast, memory‐efficient, easy to code and it can be adapted straightforwardly in the standard screen‐space rendering methods, even in GPU architectures. We show through a set of experiments how the prior screen‐space techniques can be benefited and improved by our approach.
Transforming an Adjacency Graph into Dimensioned Floorplan Layouts
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Bisht, Sumit; Shekhawat, Krishnendra; Upasani, Nitant; Jain, Rahil N.; Tiwaskar, Riddhesh Jayesh; Hebbar, Chinmay; Hauser, Helwig and Alliez, Pierre
In recent times, researchers have proposed several approaches for building floorplans using parametric/generative design, shape grammars, machine learning, AI, . This paper aims to demonstrate a mathematical approach for the automated generation of floorplan layouts. Mathematical formulations warrant the fulfilment of all input user constraints, unlike the learning‐based methods present in the literature. Moreover, the algorithms illustrated in this paper are robust, scalable and highly efficient, generating thousands of floorplans in a few milliseconds.We present G2PLAN, a software based on graph‐theoretic and linear optimization techniques, that generates all topologically distinct floorplans with different boundary rooms in linear time for given adjacency and dimensional constraints. G2PLAN builds on the work of GPLAN and offers solutions to a wider range of adjacency relations (one‐connected, non‐triangulated graphs) and better dimensioning customizability. It also generates a catalogue of dimensionless as well as dimensioned floorplans satisfying user requirements.
Harmonics Virtual Lights: Fast Projection of Luminance Field on Spherical Harmonics for Efficient Rendering
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Mézières, Pierre; Desrichard, François; Vanderhaeghe, David; Paulin, Mathias; Hauser, Helwig and Alliez, Pierre
In this paper, we introduce harmonics virtual lights (HVL), to model indirect light sources for interactive global illumination of dynamic 3D scenes. Virtual point lights (VPL) are an efficient approach to define indirect light sources and to evaluate the resulting indirect lighting. Nonetheless, VPL suffer from disturbing artefacts, especially with high‐frequency materials. Virtual spherical lights (VSL) avoid these artefacts by considering spheres instead of points but estimates the lighting integral using Monte‐Carlo which results to noise in the final image. We define HVL as an extension of VSL in a spherical harmonics (SH) framework, defining a closed form of the lighting integral evaluation. We propose an efficient SH projection of spherical lights contribution faster than existing methods. Computing the outgoing luminance requires operations when using materials with circular symmetric lobes, and operations for the general case, where is the number of SH bands. HVL can be used with either parametric or measured BRDF without extra cost and offers control over rendering time and image quality, by either decreasing or increasing the band limit used for SH projection. Our approach is particularly well‐designed to render medium‐frequency one‐bounce global illumination with arbitrary BRDF at an interactive frame rate.
Gaussian Process for Radiance Functions on the S2$\mathbb {S}^2$ Sphere
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Marques, R.; Bouville, C.; Bouatouch, K.; Hauser, Helwig and Alliez, Pierre
Efficient approximation of incident radiance functions from a set of samples is still an open problem in physically based rendering. Indeed, most of the computing power required to synthesize a photo‐realistic image is devoted to collecting samples of the incident radiance function, which are necessary to provide an estimate of the rendering equation solution. Due to the large number of samples required to reach a high‐quality estimate, this process is usually tedious and can take up to several days. In this paper, we focus on the problem of approximation of incident radiance functions on the sphere. To this end, we resort to a Gaussian Process (GP), a highly flexible function modelling tool, which has received little attention in rendering. We make an extensive analysis of the application of GPs to incident radiance functions, addressing crucial issues such as robust hyperparameter learning, or selecting the covariance function which better suits incident radiance functions. Our analysis is both theoretical and experimental. Furthermore, it provides a seamless connection between the original spherical domain and the spectral domain, on which we build to derive a method for fast computation and rotation of spherical harmonics coefficients.
Delaunay Painting: Perceptual Image Colouring from Raster Contours with Gaps
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Parakkat, Amal Dev; Memari, Pooran; Cani, Marie‐Paule; Hauser, Helwig and Alliez, Pierre
We introduce Delaunay Painting, a novel and easy‐to‐use method to flat‐colour contour‐sketches with gaps. Starting from a Delaunay triangulation of the input contours, triangles are iteratively filled with the appropriate colours, thanks to the dynamic update of flow values calculated from colour hints. Aesthetic finish is then achieved, through energy minimisation of contour‐curves and further heuristics enforcing the appropriate sharp corners. To be more efficient, the user can also make use of our colour diffusion framework, which automatically extends colouring to small, internal regions such as those delimited by hatches. The resulting method robustly handles input contours with strong gaps. As an interactive tool, it minimizes user's efforts and enables any colouring strategy, as the result does not depend on the order of interactions. We also provide an automatized version of the colouring strategy for quick segmentation of contours images, that we illustrate with applications to medical imaging and sketch segmentation.
Visual Analytics of Multivariate Intensive Care Time Series Data
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Brich, N.; Schulz, C.; Peter, J.; Klingert, W.; Schenk, M.; Weiskopf, D.; Krone, M.; Hauser, Helwig and Alliez, Pierre
We present an approach for visual analysis of high‐dimensional measurement data with varying sampling rates as routinely recorded in intensive care units. In intensive care, most assessments not only depend on one single measurement but a plethora of mixed measurements over time. Even for trained experts, efficient and accurate analysis of such multivariate data remains a challenging task. We present a linked‐view post hoc visual analytics application that reduces data complexity by combining projection‐based time curves for overview with small multiples for details on demand. Our approach supports not only the analysis of individual patients but also of ensembles by adapting existing techniques using non‐parametric statistics. We evaluated the effectiveness and acceptance of our approach through expert feedback with domain scientists from the surgical department using real‐world data: a post‐surgery study performed on a porcine surrogate model to identify parameters suitable for diagnosing and prognosticating the volume state, and clinical data from a public database. The results show that our approach allows for detailed analysis of changes in patient state while also summarizing the temporal development of the overall condition.
Simplification of 2D Polygonal Partitions via Point‐line Projective Duality, and Application to Urban Reconstruction
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Vuillamy, J.; Lieutier, A.; Lafarge, F.; Alliez, P.; Hauser, Helwig and Alliez, Pierre
We address the problem of simplifying two‐dimensional polygonal partitions that exhibit strong regularities. Such partitions are relevant for reconstructing urban scenes in a concise way. Preserving long linear structures spanning several partition cells motivates a point‐line projective duality approach in which points represent line intersections, and lines possibly carry multiple points. We propose a simplification algorithm that seeks a balance between the fidelity to the input partition, the enforcement of canonical relationships between lines (orthogonality or parallelism) and a low complexity output. Our methodology alternates continuous optimization by Riemannian gradient descent with combinatorial reduction, resulting in a progressive simplification scheme. Our experiments show that preserving canonical relationships helps gracefully degrade partitions of urban scenes, and yields more concise and regularity‐preserving meshes than common mesh‐based simplification approaches.
Computing Schematic Layouts for Spatial Hypergraphs on Concentric Circles and Grids
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Bekos, M.A.; Dekker, D.J.C.; Frank, F.; Meulemans, W.; Rodgers, P.; Schulz, A.; Wessel, S.; Hauser, Helwig and Alliez, Pierre
Set systems can be visualized in various ways. An important distinction between techniques is whether the elements have a spatial location that is to be used for the visualization; for example, the elements are cities on a map. Strictly adhering to such location may severely limit the visualization and force overlay, intersections and other forms of clutter. On the other hand, completely ignoring the spatial dimension omits information and may hide spatial patterns in the data. We study layouts for set systems (or hypergraphs) in which spatial locations are displaced onto concentric circles or a grid, to obtain schematic set visualizations. We investigate the tractability of the underlying algorithmic problems adopting different optimization criteria (e.g. crossings or bends) for the layout structure, also known as the support of the hypergraph. Furthermore, we describe a simulated‐annealing approach to heuristically optimize a combination of such criteria. Using this method in computational experiments, we explore the trade‐offs and dependencies between criteria for computing high‐quality schematic set visualizations.
Seeking Patterns of Visual Pattern Discovery for Knowledge Building
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Andrienko, N.; Andrienko, G.; Chen, S.; Fisher, B.; Hauser, Helwig and Alliez, Pierre
Currently, the methodological and technical developments in visual analytics, as well as the existing theories, are not sufficiently grounded by empirical studies that can provide an understanding of the processes of visual data analysis, analytical reasoning and derivation of new knowledge by humans. We conducted an exploratory empirical study in which participants analysed complex and data‐rich visualisations by detecting salient visual patterns, translating them into conceptual information structures and reasoning about those structures to construct an overall understanding of the analysis subject. Eye tracking and voice recording were used to capture this process. We analysed how the data we had collected match several existing theoretical models intended to describe visualisation‐supported reasoning, knowledge building, decision making or use and development of mental models. We found that none of these theoretical models alone is sufficient for describing the processes of visual analysis and knowledge generation that we observed in our experiments, whereas a combination of three particular models could be apposite. We also pondered whether empirical studies like ours can be used to derive implications and recommendations for possible ways to support users of visual analytics systems. Our approaches to designing and conducting the experiments and analysing the empirical data were appropriate to the goals of the study and can be recommended for use in other empirical studies in visual analytics.
JOKR: Joint Keypoint Representation for Unsupervised Video Retargeting
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Mokady, R.; Tzaban, R.; Benaim, S.; Bermano, A.H.; Cohen‐Or, D.; Hauser, Helwig and Alliez, Pierre
In unsupervised video retargeting, content is transferred from one video to another while preserving the original appearance and style, without any additional annotations. While this challenge has seen substantial advancements through the use of deep neural networks, current methods struggle when the source and target videos are of shapes that are different in limb lengths or other body proportions. In this work, we consider this task for the case of objects of different shapes and appearances, that consist of similar skeleton connectivity and depict similar motion. We introduce JOKR—a JOint Keypoint Representation that captures the geometry common to both videos, while being disentangled from their unique styles. Our model first extracts unsupervised keypoints from the given videos. From this representation, two decoders reconstruct geometry and appearance, one for each of the input sequences. By employing an affine‐invariant domain confusion term over the keypoints bottleneck, we enforce the unsupervised keypoint representations of both videos to be indistinguishable. This encourages the aforementioned disentanglement between motion and appearance, mapping similar poses from both domains to the same representation. This allows yielding a sequence with the appearance and style of one video, but the content of the other. Our applicability is demonstrated through challenging video pairs compared to state‐of‐the‐art methods. Furthermore, we demonstrate that this geometry‐driven representation enables intuitive control, such as temporal coherence and manual pose editing. Videos can be viewed in the supplement HTML.
RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Eirich, J.; Münch, M.; Jäckle, D.; Sedlmair, M.; Bonart, J.; Schreck, T.; Hauser, Helwig and Alliez, Pierre
Random Forests (RFs) are a machine learning (ML) technique widely used across industries. The interpretation of a given RF usually relies on the analysis of statistical values and is often only possible for data analytics experts. To make RFs accessible to experts with no data analytics background, we present RfX, a Visual Analytics (VA) system for the analysis of a RF's decision‐making process. RfX allows to interactively analyse the properties of a forest and to explore and compare multiple trees in a RF. Thus, its users can identify relationships within a RF's feature subspace and detect hidden patterns in the model's underlying data. We contribute a design study in collaboration with an automotive company. A formative evaluation of RFX was carried out with two domain experts and a summative evaluation in the form of a field study with five domain experts. In this context, new hidden patterns such as increased eccentricities in an engine's rotor by observing secondary excitations of its bearings were detected using analyses made with RfX. Rules derived from analyses with the system led to a change in the company's testing procedures for electrical engines, which resulted in 80% reduced testing time for over 30% of all components.
Non‐Isometric Shape Matching via Functional Maps on Landmark‐Adapted Bases
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Panine, Mikhail; Kirgo, Maxime; Ovsjanikov, Maks; Hauser, Helwig and Alliez, Pierre
We propose a principled approach for non‐isometric landmark‐preserving non‐rigid shape matching. Our method is based on the functional map framework, but rather than promoting isometries we focus on near‐conformal maps that preserve landmarks exactly. We achieve this, first, by introducing a novel landmark‐adapted basis using an intrinsic Dirichlet‐Steklov eigenproblem. Second, we establish the functional decomposition of conformal maps expressed in this basis. Finally, we formulate a conformally‐invariant energy that promotes high‐quality landmark‐preserving maps, and show how it can be optimized via a variant of the recently proposed ZoomOut method that we extend to our setting. Our method is descriptor‐free, efficient and robust to significant mesh variability. We evaluate our approach on a range of benchmark datasets and demonstrate state‐of‐the‐art performance on non‐isometric benchmarks and near state‐of‐the‐art performance on isometric ones.
Learning Human Viewpoint Preferences from Sparsely Annotated Models
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Hartwig, S.; Schelling, M.; Onzenoodt, C. v.; Vázquez, P.‐P.; Hermosilla, P.; Ropinski, T.; Hauser, Helwig and Alliez, Pierre
View quality measures compute scores for given views and are used to determine an optimal view in viewpoint selection tasks. Unfortunately, despite the wide adoption of these measures, they are rather based on computational quantities, such as entropy, than human preferences. To instead tailor viewpoint measures towards humans, view quality measures need to be able to capture human viewpoint preferences. Therefore, we introduce a large‐scale crowdsourced data set, which contains 58 annotated viewpoints for 3220 ModelNet40 models. Based on this data, we derive a neural view quality measure abiding to human preferences. We further demonstrate that this view quality measure not only generalizes to models unseen during training, but also to unseen model categories. We are thus able to predict view qualities for single images, and directly predict human preferred viewpoints for 3D models by exploiting point‐based learning technology, without requiring to generate intermediate images or sampling the view sphere. We will detail our data collection procedure, describe the data analysis and model training and will evaluate the predictive quality of our trained viewpoint measure on unseen models and categories. To our knowledge, this is the first deep learning approach to predict a view quality measure solely based on human preferences.
Event‐based Dynamic Graph Drawing without the Agonizing Pain
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Arleo, A.; Miksch, S.; Archambault, D.; Hauser, Helwig and Alliez, Pierre
Temporal networks can naturally model real‐world complex phenomena such as contact networks, information dissemination and physical proximity. However, nodes and edges bear real‐time coordinates, making it difficult to organize them into discrete timeslices, without a loss of temporal information due to projection. Event‐based dynamic graph drawing rejects the notion of a timeslice and allows each node and edge to retain its own real‐valued time coordinate. While existing work has demonstrated clear advantages for this approach, they come at a running time cost. We investigate the problem of accelerating event‐based layout to make it more competitive with existing layout techniques. In this paper, we describe the design, implementation and experimental evaluation of , the first multi‐level event‐based graph layout algorithm. We consider three operators for coarsening and placement, inspired by Walshaw, GRIP and FM, which we couple with an event‐based graph drawing algorithm. We also propose two extensions to the core algorithm: and . We perform two experiments: first, we compare variants to existing state‐of‐the‐art dynamic graph layout approaches; second, we investigate the impact of each of the proposed algorithm extensions. proves to be competitive with existing approaches, and the proposed extensions achieve their design goals and contribute in opening new research directions.
Fast Neural Representations for Direct Volume Rendering
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Weiss, S.; Hermüller, P.; Westermann, R.; Hauser, Helwig and Alliez, Pierre
Despite the potential of neural scene representations to effectively compress 3D scalar fields at high reconstruction quality, the computational complexity of the training and data reconstruction step using scene representation networks limits their use in practical applications. In this paper, we analyse whether scene representation networks can be modified to reduce these limitations and whether such architectures can also be used for temporal reconstruction tasks. We propose a novel design of scene representation networks using GPU tensor cores to integrate the reconstruction seamlessly into on‐chip raytracing kernels, and compare the quality and performance of this network to alternative network‐ and non‐network‐based compression schemes. The results indicate competitive quality of our design at high compression rates, and significantly faster decoding times and lower memory consumption during data reconstruction. We investigate how density gradients can be computed using the network and show an extension where density, gradient and curvature are predicted jointly. As an alternative to spatial super‐resolution approaches for time‐varying fields, we propose a solution that builds upon latent‐space interpolation to enable random access reconstruction at arbitrary granularity. We summarize our findings in the form of an assessment of the strengths and limitations of scene representation networks for compression domain volume rendering, and outline future research directions. Source code:

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