arXiv - CS - Graphics最新文献_第7页

Subsurface Scattering for 3D Gaussian Splatting 三维高斯溅射的次表层散射

arXiv - CS - Graphics Pub Date : 2024-08-22 DOI: arxiv-2408.12282

Jan-Niklas Dihlmann, Arjun Majumdar, Andreas Engelhardt, Raphael Braun, Hendrik P. A. Lensch

{"title":"Subsurface Scattering for 3D Gaussian Splatting","authors":"Jan-Niklas Dihlmann, Arjun Majumdar, Andreas Engelhardt, Raphael Braun, Hendrik P. A. Lensch","doi":"arxiv-2408.12282","DOIUrl":"https://doi.org/arxiv-2408.12282","url":null,"abstract":"3D reconstruction and relighting of objects made from scattering materials\u0000present a significant challenge due to the complex light transport beneath the\u0000surface. 3D Gaussian Splatting introduced high-quality novel view synthesis at\u0000real-time speeds. While 3D Gaussians efficiently approximate an object's\u0000surface, they fail to capture the volumetric properties of subsurface\u0000scattering. We propose a framework for optimizing an object's shape together\u0000with the radiance transfer field given multi-view OLAT (one light at a time)\u0000data. Our method decomposes the scene into an explicit surface represented as\u00003D Gaussians, with a spatially varying BRDF, and an implicit volumetric\u0000representation of the scattering component. A learned incident light field\u0000accounts for shadowing. We optimize all parameters jointly via ray-traced\u0000differentiable rendering. Our approach enables material editing, relighting and\u0000novel view synthesis at interactive rates. We show successful application on\u0000synthetic data and introduce a newly acquired multi-view multi-light dataset of\u0000objects in a light-stage setup. Compared to previous work we achieve comparable\u0000or better results at a fraction of optimization and rendering time while\u0000enabling detailed control over material attributes. Project page\u0000https://sss.jdihlmann.com/","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pano2Room: Novel View Synthesis from a Single Indoor Panorama Pano2Room：从单一室内全景图合成新颖视图

arXiv - CS - Graphics Pub Date : 2024-08-21 DOI: arxiv-2408.11413

Guo Pu, Yiming Zhao, Zhouhui Lian

{"title":"Pano2Room: Novel View Synthesis from a Single Indoor Panorama","authors":"Guo Pu, Yiming Zhao, Zhouhui Lian","doi":"arxiv-2408.11413","DOIUrl":"https://doi.org/arxiv-2408.11413","url":null,"abstract":"Recent single-view 3D generative methods have made significant advancements\u0000by leveraging knowledge distilled from extensive 3D object datasets. However,\u0000challenges persist in the synthesis of 3D scenes from a single view, primarily\u0000due to the complexity of real-world environments and the limited availability\u0000of high-quality prior resources. In this paper, we introduce a novel approach\u0000called Pano2Room, designed to automatically reconstruct high-quality 3D indoor\u0000scenes from a single panoramic image. These panoramic images can be easily\u0000generated using a panoramic RGBD inpainter from captures at a single location\u0000with any camera. The key idea is to initially construct a preliminary mesh from\u0000the input panorama, and iteratively refine this mesh using a panoramic RGBD\u0000inpainter while collecting photo-realistic 3D-consistent pseudo novel views.\u0000Finally, the refined mesh is converted into a 3D Gaussian Splatting field and\u0000trained with the collected pseudo novel views. This pipeline enables the\u0000reconstruction of real-world 3D scenes, even in the presence of large\u0000occlusions, and facilitates the synthesis of photo-realistic novel views with\u0000detailed geometry. Extensive qualitative and quantitative experiments have been\u0000conducted to validate the superiority of our method in single-panorama indoor\u0000novel synthesis compared to the state-of-the-art. Our code and data are\u0000available at url{https://github.com/TrickyGo/Pano2Room}.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data 工业 X 射线和中子计算机断层扫描数据的双模态可视化

arXiv - CS - Graphics Pub Date : 2024-08-21 DOI: arxiv-2408.11957

Xuan Huang, Haichao Miao, Hyojin Kim, Andrew Townsend, Kyle Champley, Joseph Tringe, Valerio Pascucci, Peer-Timo Bremer

{"title":"Bimodal Visualization of Industrial X-Ray and Neutron Computed Tomography Data","authors":"Xuan Huang, Haichao Miao, Hyojin Kim, Andrew Townsend, Kyle Champley, Joseph Tringe, Valerio Pascucci, Peer-Timo Bremer","doi":"arxiv-2408.11957","DOIUrl":"https://doi.org/arxiv-2408.11957","url":null,"abstract":"Advanced manufacturing creates increasingly complex objects with material\u0000compositions that are often difficult to characterize by a single modality. Our\u0000collaborating domain scientists are going beyond traditional methods by\u0000employing both X-ray and neutron computed tomography to obtain complementary\u0000representations expected to better resolve material boundaries. However, the\u0000use of two modalities creates its own challenges for visualization, requiring\u0000either complex adjustments of bimodal transfer functions or the need for\u0000multiple views. Together with experts in nondestructive evaluation, we designed\u0000a novel interactive bimodal visualization approach to create a combined view of\u0000the co-registered X-ray and neutron acquisitions of industrial objects. Using\u0000an automatic topological segmentation of the bivariate histogram of X-ray and\u0000neutron values as a starting point, the system provides a simple yet effective\u0000interface to easily create, explore, and adjust a bimodal visualization. We\u0000propose a widget with simple brushing interactions that enables the user to\u0000quickly correct the segmented histogram results. Our semiautomated system\u0000enables domain experts to intuitively explore large bimodal datasets without\u0000the need for either advanced segmentation algorithms or knowledge of\u0000visualization techniques. We demonstrate our approach using synthetic examp","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"434 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Iterative Object Count Optimization for Text-to-image Diffusion Models 文本到图像扩散模型的迭代对象计数优化

arXiv - CS - Graphics Pub Date : 2024-08-21 DOI: arxiv-2408.11721

Oz Zafar, Lior Wolf, Idan Schwartz

{"title":"Iterative Object Count Optimization for Text-to-image Diffusion Models","authors":"Oz Zafar, Lior Wolf, Idan Schwartz","doi":"arxiv-2408.11721","DOIUrl":"https://doi.org/arxiv-2408.11721","url":null,"abstract":"We address a persistent challenge in text-to-image models: accurately\u0000generating a specified number of objects. Current models, which learn from\u0000image-text pairs, inherently struggle with counting, as training data cannot\u0000depict every possible number of objects for any given object. To solve this, we\u0000propose optimizing the generated image based on a counting loss derived from a\u0000counting model that aggregates an object's potential. Employing an\u0000out-of-the-box counting model is challenging for two reasons: first, the model\u0000requires a scaling hyperparameter for the potential aggregation that varies\u0000depending on the viewpoint of the objects, and second, classifier guidance\u0000techniques require modified models that operate on noisy intermediate diffusion\u0000steps. To address these challenges, we propose an iterated online training mode\u0000that improves the accuracy of inferred images while altering the text\u0000conditioning embedding and dynamically adjusting hyperparameters. Our method\u0000offers three key advantages: (i) it can consider non-derivable counting\u0000techniques based on detection models, (ii) it is a zero-shot plug-and-play\u0000solution facilitating rapid changes to the counting techniques and image\u0000generation methods, and (iii) the optimized counting token can be reused to\u0000generate accurate images without additional optimization. We evaluate the\u0000generation of various objects and show significant improvements in accuracy.\u0000The project page is available at https://ozzafar.github.io/count_token.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEGAS: Detailed Expressions on Full-Body Gaussian Avatars DEGAS：全身高斯头像的详细表达

arXiv - CS - Graphics Pub Date : 2024-08-20 DOI: arxiv-2408.10588

Zhijing Shao, Duotun Wang, Qing-Yao Tian, Yao-Dong Yang, Hengyu Meng, Zeyu Cai, Bo Dong, Yu Zhang, Kang Zhang, Zeyu Wang

{"title":"DEGAS: Detailed Expressions on Full-Body Gaussian Avatars","authors":"Zhijing Shao, Duotun Wang, Qing-Yao Tian, Yao-Dong Yang, Hengyu Meng, Zeyu Cai, Bo Dong, Yu Zhang, Kang Zhang, Zeyu Wang","doi":"arxiv-2408.10588","DOIUrl":"https://doi.org/arxiv-2408.10588","url":null,"abstract":"Although neural rendering has made significant advancements in creating\u0000lifelike, animatable full-body and head avatars, incorporating detailed\u0000expressions into full-body avatars remains largely unexplored. We present\u0000DEGAS, the first 3D Gaussian Splatting (3DGS)-based modeling method for\u0000full-body avatars with rich facial expressions. Trained on multiview videos of\u0000a given subject, our method learns a conditional variational autoencoder that\u0000takes both the body motion and facial expression as driving signals to generate\u0000Gaussian maps in the UV layout. To drive the facial expressions, instead of the\u0000commonly used 3D Morphable Models (3DMMs) in 3D head avatars, we propose to\u0000adopt the expression latent space trained solely on 2D portrait images,\u0000bridging the gap between 2D talking faces and 3D avatars. Leveraging the\u0000rendering capability of 3DGS and the rich expressiveness of the expression\u0000latent space, the learned avatars can be reenacted to reproduce photorealistic\u0000rendering images with subtle and accurate facial expressions. Experiments on an\u0000existing dataset and our newly proposed dataset of full-body talking avatars\u0000demonstrate the efficacy of our method. We also propose an audio-driven\u0000extension of our method with the help of 2D talking faces, opening new\u0000possibilities to interactive AI agents.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model MeshFormer：利用三维引导重建模型生成高质量网格

arXiv - CS - Graphics Pub Date : 2024-08-19 DOI: arxiv-2408.10198

Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, Hongzhi Wu, Hao Su

{"title":"MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model","authors":"Minghua Liu, Chong Zeng, Xinyue Wei, Ruoxi Shi, Linghao Chen, Chao Xu, Mengqi Zhang, Zhaoning Wang, Xiaoshuai Zhang, Isabella Liu, Hongzhi Wu, Hao Su","doi":"arxiv-2408.10198","DOIUrl":"https://doi.org/arxiv-2408.10198","url":null,"abstract":"Open-world 3D reconstruction models have recently garnered significant\u0000attention. However, without sufficient 3D inductive bias, existing methods\u0000typically entail expensive training costs and struggle to extract high-quality\u00003D meshes. In this work, we introduce MeshFormer, a sparse-view reconstruction\u0000model that explicitly leverages 3D native structure, input guidance, and\u0000training supervision. Specifically, instead of using a triplane representation,\u0000we store features in 3D sparse voxels and combine transformers with 3D\u0000convolutions to leverage an explicit 3D structure and projective bias. In\u0000addition to sparse-view RGB input, we require the network to take input and\u0000generate corresponding normal maps. The input normal maps can be predicted by\u00002D diffusion models, significantly aiding in the guidance and refinement of the\u0000geometry's learning. Moreover, by combining Signed Distance Function (SDF)\u0000supervision with surface rendering, we directly learn to generate high-quality\u0000meshes without the need for complex multi-stage training processes. By\u0000incorporating these explicit 3D biases, MeshFormer can be trained efficiently\u0000and deliver high-quality textured meshes with fine-grained geometric details.\u0000It can also be integrated with 2D diffusion models to enable fast\u0000single-image-to-3D and text-to-3D tasks. Project page:\u0000https://meshformer3d.github.io","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neural Representation of Shape-Dependent Laplacian Eigenfunctions 形状相关拉普拉奇特征函数的神经表征

arXiv - CS - Graphics Pub Date : 2024-08-19 DOI: arxiv-2408.10099

Yue Chang, Otman Benchekroun, Maurizio M. Chiaramonte, Peter Yichen Chen, Eitan Grinspun

{"title":"Neural Representation of Shape-Dependent Laplacian Eigenfunctions","authors":"Yue Chang, Otman Benchekroun, Maurizio M. Chiaramonte, Peter Yichen Chen, Eitan Grinspun","doi":"arxiv-2408.10099","DOIUrl":"https://doi.org/arxiv-2408.10099","url":null,"abstract":"The eigenfunctions of the Laplace operator are essential in mathematical\u0000physics, engineering, and geometry processing. Typically, these are computed by\u0000discretizing the domain and performing eigendecomposition, tying the results to\u0000a specific mesh. However, this method is unsuitable for\u0000continuously-parameterized shapes. We propose a novel representation for eigenfunctions in\u0000continuously-parameterized shape spaces, where eigenfunctions are spatial\u0000fields with continuous dependence on shape parameters, defined by minimal\u0000Dirichlet energy, unit norm, and mutual orthogonality. We implement this with\u0000multilayer perceptrons trained as neural fields, mapping shape parameters and\u0000domain positions to eigenfunction values. A unique challenge is enforcing mutual orthogonality with respect to\u0000causality, where the causal ordering varies across the shape space. Our\u0000training method therefore requires three interwoven concepts: (1) learning $n$\u0000eigenfunctions concurrently by minimizing Dirichlet energy with unit norm\u0000constraints; (2) filtering gradients during backpropagation to enforce causal\u0000orthogonality, preventing earlier eigenfunctions from being influenced by later\u0000ones; (3) dynamically sorting the causal ordering based on eigenvalues to track\u0000eigenvalue curve crossovers. We demonstrate our method on problems such as shape family analysis,\u0000predicting eigenfunctions for incomplete shapes, interactive shape\u0000manipulation, and computing higher-dimensional eigenfunctions, on all of which\u0000traditional methods fall short.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"284 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Double-Precision Floating-Point Data Visualizations Using Vulkan API 使用 Vulkan API 实现双精度浮点数据可视化

arXiv - CS - Graphics Pub Date : 2024-08-19 DOI: arxiv-2408.09699

Nezihe Sozen

{"title":"Double-Precision Floating-Point Data Visualizations Using Vulkan API","authors":"Nezihe Sozen","doi":"arxiv-2408.09699","DOIUrl":"https://doi.org/arxiv-2408.09699","url":null,"abstract":"Proper representation of data in graphical visualizations becomes challenging\u0000when high accuracy in data types is required, especially in those situations\u0000where the difference between double-precision floating-point and\u0000single-precision floating-point values makes a significant difference. Some of\u0000the limitations of using single-precision over double-precision include lesser\u0000accuracy, which accumulates errors over time, and poor modeling of large or\u0000small numbers. In such scenarios, emulated double precision is often used as a\u0000solution. The proposed methodology uses a modern GPU pipeline and graphics\u0000library API specifications to use native double precision. In this research,\u0000the approach is implemented using the Vulkan API, C++, and GLSL. Experimental\u0000evaluation with a series of experiments on 2D and 3D point datasets is proposed\u0000to indicate the effectiveness of the approach. This evaluates performance\u0000comparisons between native double-precision implementations against their\u0000emulated double-precision approaches with respect to rendering performance and\u0000accuracy. This study provides insight into the benefits of using native\u0000double-precision in graphical applications, denoting limitations and problems\u0000with emulated double-precision usages. These results improve the general\u0000understanding of the precision involved in graphical visualizations and assist\u0000developers in making decisions about which precision methods to use during\u0000their applications.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"40 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unified Smooth Vector Graphics: Modeling Gradient Meshes and Curve-based Approaches Jointly as Poisson Problem 统一平滑矢量图形：将梯度网格和基于曲线的方法联合建模为泊松问题

arXiv - CS - Graphics Pub Date : 2024-08-17 DOI: arxiv-2408.09211

Xingze Tian, Tobias Günther

{"title":"Unified Smooth Vector Graphics: Modeling Gradient Meshes and Curve-based Approaches Jointly as Poisson Problem","authors":"Xingze Tian, Tobias Günther","doi":"arxiv-2408.09211","DOIUrl":"https://doi.org/arxiv-2408.09211","url":null,"abstract":"Research on smooth vector graphics is separated into two independent research\u0000threads: one on interpolation-based gradient meshes and the other on\u0000diffusion-based curve formulations. With this paper, we propose a mathematical\u0000formulation that unifies gradient meshes and curve-based approaches as solution\u0000to a Poisson problem. To combine these two well-known representations, we first\u0000generate a non-overlapping intermediate patch representation that specifies for\u0000each patch a target Laplacian and boundary conditions. Unifying the treatment\u0000of boundary conditions adds further artistic degrees of freedoms to the\u0000existing formulations, such as Neumann conditions on diffusion curves. To\u0000synthesize a raster image for a given output resolution, we then rasterize\u0000boundary conditions and Laplacians for the respective patches and compute the\u0000final image as solution to a Poisson problem. We evaluate the method on various\u0000test scenes containing gradient meshes and curve-based primitives. Since our\u0000mathematical formulation works with established smooth vector graphics\u0000primitives on the front-end, it is compatible with existing content creation\u0000pipelines and with established editing tools. Rather than continuing two\u0000separate research paths, we hope that a unification of the formulations will\u0000lead to new rasterization and vectorization tools in the future that utilize\u0000the strengths of both approaches.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142221936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Localized Evaluation for Constructing Discrete Vector Fields 构建离散矢量场的局部评估

arXiv - CS - Graphics Pub Date : 2024-08-08 DOI: arxiv-2408.04769

Tanner Finken, Julien Tierny, Joshua A Levine

{"title":"Localized Evaluation for Constructing Discrete Vector Fields","authors":"Tanner Finken, Julien Tierny, Joshua A Levine","doi":"arxiv-2408.04769","DOIUrl":"https://doi.org/arxiv-2408.04769","url":null,"abstract":"Topological abstractions offer a method to summarize the behavior of vector\u0000fields but computing them robustly can be challenging due to numerical\u0000precision issues. One alternative is to represent the vector field using a\u0000discrete approach, which constructs a collection of pairs of simplices in the\u0000input mesh that satisfies criteria introduced by Forman's discrete Morse\u0000theory. While numerous approaches exist to compute pairs in the restricted case\u0000of the gradient of a scalar field, state-of-the-art algorithms for the general\u0000case of vector fields require expensive optimization procedures. This paper\u0000introduces a fast, novel approach for pairing simplices of two-dimensional,\u0000triangulated vector fields that do not vary in time. The key insight of our\u0000approach is that we can employ a local evaluation, inspired by the approach\u0000used to construct a discrete gradient field, where every simplex in a mesh is\u0000considered by no more than one of its vertices. Specifically, we observe that\u0000for any edge in the input mesh, we can uniquely assign an outward direction of\u0000flow. We can further expand this consistent notion of outward flow at each\u0000vertex, which corresponds to the concept of a downhill flow in the case of\u0000scalar fields. Working with outward flow enables a linear-time algorithm that\u0000processes the (outward) neighborhoods of each vertex one-by-one, similar to the\u0000approach used for scalar fields. We couple our approach to constructing\u0000discrete vector fields with a method to extract, simplify, and visualize\u0000topological features. Empirical results on analytic and simulation data\u0000demonstrate drastic improvements in running time, produce features similar to\u0000the current state-of-the-art, and show the application of simplification to\u0000large, complex flows.","PeriodicalId":501174,"journal":{"name":"arXiv - CS - Graphics","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141932615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0