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Now showing 1 - 10 of 15
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    Complex Functional Maps: A Conformal Link Between Tangent Bundles
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Donati, Nicolas; Corman, Etienne; Melzi, Simone; Ovsjanikov, Maks; Hauser, Helwig and Alliez, Pierre
    In this paper, we introduce complex functional maps, which extend the functional map framework to conformal maps between tangent vector fields on surfaces. A key property of these maps is their . More specifically, we demonstrate that unlike regular functional maps that link of two manifolds, our complex functional maps establish a link between , thus permitting robust and efficient transfer of tangent vector fields. By first endowing and then exploiting the tangent bundle of each shape with a complex structure, the resulting operations become naturally orientation‐aware, thus favouring across shapes, without relying on descriptors or extra regularization. Finally, and perhaps more importantly, we demonstrate how these objects enable several practical applications within the functional map framework. We show that functional maps and their complex counterparts can be estimated jointly to promote orientation preservation, regularizing pipelines that previously suffered from orientation‐reversing symmetry errors.
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    Using Position‐Based Dynamics for Simulating Mitral Valve Closure and Repair Procedures
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Walczak, Lars; Georgii, Joachim; Tautz, Lennart; Neugebauer, Mathias; Wamala, Isaac; Sündermann, Simon; Falk, Volkmar; Hennemuth, Anja; Hauser, Helwig and Alliez, Pierre
    To achieve the best treatment of mitral valve disease in a patient, surgeons aim to optimally combine complementary surgical techniques. Image‐based simulation as well as visualization of the mitral valve dynamics can support the visual analysis of the patient‐specific valvular dynamics and enable an exploration of different therapy options. The usage in a time‐constrained clinical environment requires a mitral valve model that is cost‐effective, easy to set up, parameterize and evaluate. Working towards this goal, we develop a simplified model of the mitral valve and analyse its applicability for the sketched use‐case. We propose a novel approach to simulate the mitral valve with position‐based dynamics. The resulting mitral valve model can be deformed to simulate the closing and opening, and incorporate changes caused by virtual interventions in the simulation. Ten mitral valves were reconstructed from transesophageal echocardiogram sequences of patients with normal and abnormal physiology for evaluation. Simulation results showed good agreements with expert annotations of the original image data and reproduced valve closure in all cases. In four of five pathological cases, abnormal closing behaviour was correctly reproduced. In future research, we aim to improve the parameterization of the model in terms of biomechanical correctness and perform a more extensive validation.
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    Rigid Registration of Point Clouds Based on Partial Optimal Transport
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Qin, Hongxing; Zhang, Yucheng; Liu, Zhentao; Chen, Baoquan; Hauser, Helwig and Alliez, Pierre
    For rigid point cloud data registration, algorithms based on soft correspondences are more robust than the traditional ICP method and its variants. However, point clouds with severe outliers and missing data may lead to imprecise many‐to‐many correspondences and consequently inaccurate registration. In this study, we propose a point cloud registration algorithm based on partial optimal transport via a hard marginal constraint. The hard marginal constraint provides an explicit parameter to adjust the ratio of points that should be accurately matched, and helps avoid incorrect many‐to‐many correspondences. Experiments show that the proposed method achieves state‐of‐the‐art registration results when dealing with point clouds with significant amount of outliers and missing points (see ).
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    NeRF‐Tex: Neural Reflectance Field Textures
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Baatz, H.; Granskog, J.; Papas, M.; Rousselle, F.; Novák, J.; Hauser, Helwig and Alliez, Pierre
    We investigate the use of neural fields for modelling diverse mesoscale structures, such as fur, fabric and grass. Instead of using classical graphics primitives to model the structure, we propose to employ a versatile volumetric primitive represented by a neural field (NeRF‐Tex), which jointly models the geometry of the material and its response to lighting. The NeRF‐Tex primitive can be instantiated over a base mesh to ‘texture’ it with the desired meso and microscale appearance. We condition the reflectance field on user‐defined parameters that control the appearance. A single NeRF texture thus captures an entire space of reflectance fields rather than one specific structure. This increases the gamut of appearances that can be modelled and provides a solution for combating repetitive texturing artifacts. We also demonstrate that NeRF textures naturally facilitate continuous level‐of‐detail rendering. Our approach unites the versatility and modelling power of neural networks with the artistic control needed for precise modelling of virtual scenes. While all our training data are currently synthetic, our work provides a recipe that can be further extended to extract complex, hard‐to‐model appearances from real images.
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    Reconstructing Recognizable 3D Face Shapes based on 3D Morphable Models
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Jiang, Diqiong; Jin, Yiwei; Zhang, Fang‐Lue; Lai, Yu‐Kun; Deng, Risheng; Tong, Ruofeng; Tang, Min; Hauser, Helwig and Alliez, Pierre
    Many recent works have reconstructed distinctive 3D face shapes by aggregating shape parameters of the same identity and separating those of different people based on parametric models (e.g. 3D morphable models (3DMMs)). However, despite the high accuracy in the face recognition task using these shape parameters, the visual discrimination of face shapes reconstructed from those parameters remains unsatisfactory. Previous works have not answered the following research question: Do discriminative shape parameters guarantee visual discrimination in represented 3D face shapes? This paper analyses the relationship between shape parameters and reconstructed shape geometry, and proposes a novel shape identity‐aware regularization (SIR) loss for shape parameters, aiming at increasing discriminability in both the shape parameter and shape geometry domains. Moreover, to cope with the lack of training data containing both landmark and identity annotations, we propose a network structure and an associated training strategy to leverage mixed data containing either identity or landmark labels. In addition, since face recognition accuracy does not mean the recognizability of reconstructed face shapes from the shape parameters, we propose the SIR metric to measure the discriminability of face shapes. We compare our method with existing methods in terms of the reconstruction error, visual discriminability, and face recognition accuracy of the shape parameters and SIR metric. Experimental results show that our method outperforms the state‐of‐the‐art methods. The code will be released at .
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    Image Representation on Curved Optimal Triangulation
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Xiao, Yanyang; Cao, Juan; Chen, Zhonggui; Hauser, Helwig and Alliez, Pierre
    Image triangulation aims to generate an optimal partition with triangular elements to represent the given image. One bottleneck in ensuring approximation quality between the original image and a piecewise approximation over the triangulation is the inaccurate alignment of straight edges to the curved features. In this paper, we propose a novel variational method called curved optimal triangulation, where not all edges are straight segments, but may also be quadratic Bézier curves. The energy function is defined as the total approximation error determined by vertex locations, connectivity and bending of edges. The gradient formulas of this function are derived explicitly in closed form to optimize the energy function efficiently. We test our method on several models to demonstrate its efficacy and ability in preserving features. We also explore its applications in the automatic generation of stylization and Lowpoly images. With the same number of vertices, our curved optimal triangulation method generates more accurate and visually pleasing results compared with previous methods that only use straight segments.
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    Evocube: A Genetic Labelling Framework for Polycube‐Maps
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Dumery, C.; Protais, F.; Mestrallet, S.; Bourcier, C.; Ledoux, F.; Hauser, Helwig and Alliez, Pierre
    Polycube‐maps are used as base‐complexes in various fields of computational geometry, including the generation of regular all‐hexahedral meshes free of internal singularities. However, the strict alignment constraints behind polycube‐based methods make their computation challenging for CAD models used in numerical simulation via finite element method (FEM). We propose a novel approach based on an evolutionary algorithm to robustly compute polycube‐maps in this context.We address the labelling problem, which aims to precompute polycube alignment by assigning one of the base axes to each boundary face on the input. Previous research has described ways to initialize and improve a labelling via greedy local fixes. However, such algorithms lack robustness and often converge to inaccurate solutions for complex geometries. Our proposed framework alleviates this issue by embedding labelling operations in an evolutionary heuristic, defining fitness, crossover, and mutations in the context of labelling optimization. We evaluate our method on a thousand smooth and CAD meshes, showing Evocube converges to accurate labellings on a wide range of shapes. The limitations of our method are also discussed thoroughly.
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    CVFont: Synthesizing Chinese Vector Fonts via Deep Layout Inferring
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Lian, Zhouhui; Gao, Yichen; Hauser, Helwig and Alliez, Pierre
    Creating a high‐quality Chinese vector font library, which can be directly used in real applications is time‐consuming and costly, since the font library typically consists of large amounts of vector glyphs. To address this problem, we propose a data‐driven system in which only a small number (about 10%) of Chinese glyphs need to be designed. Specifically, the system first automatically decomposes those input glyphs into vector components. Then, a layout prediction module based on deep neural networks is applied to learn the layout style of input characters. Finally, proper components are selected to assemble the glyph of each unseen character based on the predicted layout to build the font library that can be directly used in computers and smart mobile devices. Experimental results demonstrate that our system synthesizes high‐quality glyphs and significantly enhances the producing efficiency of Chinese vector fonts.
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    SGLBP: Subgraph‐based Local Binary Patterns for Feature Extraction on Point Clouds
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2022) Guo, Bao; Zhang, Yuhe; Gao, Jian; Li, Chunhui; Hu, Yao; Hauser, Helwig and Alliez, Pierre
    Extraction for points that can outline the shape of a point cloud is an important task for point cloud processing in various applications. The topology information of the neighbourhood of a point usually contains sufficient information for detecting features, which is fully considered in this study. Therefore, a novel method for extracting feature points based on the topology information is proposed. First, an improved ‐shape technique is introduced, generating two graphs for potential feature detection and neighbourhood description, respectively. Local binary pattern (LBP) is then applied to the subgraphs, thus subgraph‐based local binary patterns (SGLBPs) are generated for encoding the topology of the neighbourhoods of points, which helps to remove non‐feature points from potential feature points. The proposed method can directly process raw point clouds and needs no prior surface reconstruction or geometric invariants computation; furthermore, the proposed method detects feature points by analysing the topologies of the neighbourhoods of points, consequently promoting the effectiveness for tiny features and the robustness to noises and non‐uniformly sampling patterns. The experimental results demonstrate that the proposed method is robust and achieves state‐of‐the‐art performance.
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    Seamless Parametrization of Spheres with Controlled Singularities
    (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Levi, Zohar; Hauser, Helwig and Alliez, Pierre
    We present a method for constructing seamless parametrization for genus‐0 surfaces, which can handle any feasible cone configuration, thus allowing users to arbitrarily design and tailor a mapping as desired. The method directly constructs a self‐overlapping metapolygon of the domain boundary of the mapped cut mesh, which relieves the need of using an auxiliary surface. This simplifies the pipeline and allows for a necessary optimization of the boundary polygon before mapping the interior. Moreover, it enables handling larger meshes with more cones than previous methods can handle. Our construction is purely combinatorial, and it guarantees that the mapping is locally injective – a prerequisite to today's advanced optimization methods. This is achieved via careful construction of a simple domain boundary polygon, where existence of such a polygon is proven for all cases. We offer a numerically robust algorithm to automate the construction, which involves a solution of two linear problems. We offer a full pipeline, suggesting elegant solutions to sub‐problems, and demonstrate robustness through extensive experiments.