Graphical Models最新文献_第8页

Untangling all-hex meshes via adaptive boundary optimization 通过自适应边界优化解开所有六边形网格

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-05-01 DOI: 10.1016/j.gmod.2022.101136

Qing Huang, Wen-Xiang Zhang, Qi Wang, Ligang Liu, Xiao-Ming Fu

{"title":"Untangling all-hex meshes via adaptive boundary optimization","authors":"Qing Huang, Wen-Xiang Zhang, Qi Wang, Ligang Liu, Xiao-Ming Fu","doi":"10.1016/j.gmod.2022.101136","DOIUrl":"https://doi.org/10.1016/j.gmod.2022.101136","url":null,"abstract":"<div><p>We propose a novel method to untangle and optimize all-hex meshes. Central to this algorithm is an adaptive boundary optimization process that significantly improves practical robustness. Given an all-hex mesh with many inverted hexahedral elements, we first optimize a high-quality quad boundary mesh with a small approximation<span> error to the input boundary. Since the boundary constraints limit the optimization space to search for the inversion-free meshes, we then relax the boundary constraints to generate an inversion-free all-hex mesh. We develop an adaptive boundary relaxation algorithm to implicitly restrict the shape difference between the relaxed and input boundaries, thereby facilitating the next step. Finally, an adaptive boundary difference minimization is developed to effectively and efficiently force the distance difference between the relaxed boundary and the optimized boundary of the first step to approach zero while avoiding inverted elements. We demonstrate the efficacy of our algorithm on a data set containing 1004 all-hex meshes. Compared to previous methods, our method achieves higher practical robustness.</span></p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"121 ","pages":"Article 101136"},"PeriodicalIF":1.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72219342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

TAD-Net: tooth axis detection network based on rotation transformation encoding TAD-Net:基于旋转变换编码的齿轴检测网络

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-05-01 DOI: 10.1016/j.gmod.2022.101138

Yeying Fan , Qian Ma , Guangshun Wei , Zhiming Cui , Yuanfeng Zhou , Wenping Wang

引用次数: 0

3D Printed hair modeling from strand-level hairstyles 3D打印头发造型，从发丝级别发型

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-05-01 DOI: 10.1016/j.gmod.2022.101135

Han Chen, Minghai Chen, Lin Lu

{"title":"3D Printed hair modeling from strand-level hairstyles","authors":"Han Chen, Minghai Chen, Lin Lu","doi":"10.1016/j.gmod.2022.101135","DOIUrl":"https://doi.org/10.1016/j.gmod.2022.101135","url":null,"abstract":"<div><p><span><span>Recent advances in the design and fabrication of personalized figurines have made the creation of high-quality figurines possible for ordinary users with the facilities of 3D printing<span> techniques. The hair plays an important role in gaining the realism of the figurines. Existing hair reconstruction methods suffer from the high demand for acquisition equipment, or the result is approximated very coarsely. Instead of creating hairs for figurines by scanning devices, we present a novel surface reconstruction method to generate a 3D printable hair model with geometric features from a strand-level hairstyle, thus converting the exiting digital hair database to a 3D printable database. Given a strand-level hair model, we filter the strands via bundle clustering, retain the main features, and reconstruct hair strands in two stages. First, our algorithm is the key to extracting the hair contour surface according to the structure of strands and calculating the normal for each vertex. Next, a close, manifold triangle mesh with </span></span>geometric details and an embedded </span>direction field is achieved with the Poisson surface reconstruction. We obtain closed-manifold hairstyles without user interactions, benefiting personalized figurine fabrication. We verify the feasibility of our method by exhibiting a wide range of examples.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"121 ","pages":"Article 101135"},"PeriodicalIF":1.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72219324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Point cloud denoising review: from classical to deep learning-based approaches 点云去噪综述：从经典到基于深度学习的方法

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-05-01 DOI: 10.1016/j.gmod.2022.101140

Lang Zhou , Guoxing Sun , Yong Li , Weiqing Li , Zhiyong Su

引用次数: 0

Jacobi–PIA algorithm for bi-cubic B-spline interpolation surfaces 双三次b样条插值曲面的Jacobi-PIA算法

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-03-01 DOI: 10.1016/j.gmod.2022.101134

Chengzhi Liu, Juncheng Li, Lijuan Hu

引用次数: 3

4 and 5-Axis additive manufacturing of parts represented using free-form 3D curves 4轴和5轴增材制造的零件使用自由形式的三维曲线表示

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-03-01 DOI: 10.1016/j.gmod.2022.101137

Erkan Gunpinar, Serhat Cam

{"title":"4 and 5-Axis additive manufacturing of parts represented using free-form 3D curves","authors":"Erkan Gunpinar, Serhat Cam","doi":"10.1016/j.gmod.2022.101137","DOIUrl":"10.1016/j.gmod.2022.101137","url":null,"abstract":"<div><p>Layer-by-layer additive manufacturing is commonly utilized for additive manufacturing. Recent works utilize curved layers (rather than planar ones), on which print-paths are located, and outline their advantage over planar slicing. In this paper, free-form three-dimensional curves are utilized as input for the generation of print-paths, which covers the model to be printed and do not necessarily lie on either a planar or a curved layer. Such print-paths have been recently studied for 3-axis additive manufacturing, and a novel additive manufacturing process for the models represented using such curves are proposed for 4 and 5-axis additive manufacturing in this paper. The input curves are first subdivided into short sub-curves (i.e., segments), which are then merged to obtain print-paths with (collision-free) printing-head orientations along them. Thanks to additional two rotational axes of the printing-head, a less number of print-paths can potentially be obtained, which can reduce subdivisions in the input curves, and therefore, is desirable in additive manufacturing for improved mechanical properties in the printed parts. As a proof of concept, the print-paths with printing-head orientations along them are finally validated using an AM simulator and machine.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"120 ","pages":"Article 101137"},"PeriodicalIF":1.7,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75098090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

An Approach to Preprocess and Cluster a BRDF Database BRDF数据库的预处理与聚类方法

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-01-01 DOI: 10.1016/j.gmod.2021.101123

Mislene da Silva Nunes , Methanias Colaço Júnior , Gastão Florêncio Miranda Jr. , Beatriz Trinchão Andrade

{"title":"An Approach to Preprocess and Cluster a BRDF Database","authors":"Mislene da Silva Nunes , Methanias Colaço Júnior , Gastão Florêncio Miranda Jr. , Beatriz Trinchão Andrade","doi":"10.1016/j.gmod.2021.101123","DOIUrl":"https://doi.org/10.1016/j.gmod.2021.101123","url":null,"abstract":"<div><h3>Context</h3><p>The Bidirectional Reflectance Distribution Function (BRDF) represents a material through the incoming light on its surface. In this context, material clustering contributes to selecting a basis of representative BRDFs, the reconstruction of BRDFs, the personalization of the appearance of materials, and image-based estimation of material properties.</p></div><div><h3>Objective</h3><p>This work presents an approach to cluster a BRDF database according to its reflectance features.</p></div><div><h3>Method</h3><p>We first preprocess a BRDF database by mapping it to an image slice database and then find the best parameters for the LLE method through an empirical analysis, retrieving lower-dimensional databases. We performed a controlled experiment using the k-means, k-medoids, and spectral clustering algorithms applied to the low-dimensional databases.</p></div><div><h3>Conclusion</h3><p>K-means presented the best overall result compared to the other clustering algorithms. For applications that require cluster representatives from the database, we suggest using k-medoids, which presented results close to those of the k-means.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"119 ","pages":"Article 101123"},"PeriodicalIF":1.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91764504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multi-scale and multi-level shape descriptor learning via a hybrid fusion network 基于混合融合网络的多尺度多层次形状描述符学习

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-01-01 DOI: 10.1016/j.gmod.2021.101121

Xinwei Huang , Nannan Li , Qing Xia , Shuai Li , Aimin Hao , Hong Qin

{"title":"Multi-scale and multi-level shape descriptor learning via a hybrid fusion network","authors":"Xinwei Huang , Nannan Li , Qing Xia , Shuai Li , Aimin Hao , Hong Qin","doi":"10.1016/j.gmod.2021.101121","DOIUrl":"https://doi.org/10.1016/j.gmod.2021.101121","url":null,"abstract":"<div><p><span>Discriminative and informative 3D shape<span> descriptors are of fundamental significance to computer graphics<span> applications, especially in the fields of geometry modeling and shape analysis. 3D shape descriptors, which reveal extrinsic/intrinsic properties of 3D shapes, have been well studied for decades and proved to be useful and effective in various analysis and synthesis tasks. Nonetheless, existing descriptors are mainly founded upon certain local differential attributes or global shape spectra, and certain combinations of both types. Conventional descriptors are typically customized for specific tasks with priori domain knowledge, which severely prevents their applications from widespread use. Recently, neural networks, benefiting from their powerful data-driven capability for general feature extraction from raw data without any domain knowledge, have achieved great success in many areas including shape analysis. In this paper, we present a novel hybrid fusion network (HFN) that learns multi-scale and multi-level shape representations via uniformly integrating a traditional region-based descriptor with modern neural networks. On one hand, we exploit the spectral graph wavelets (SGWs) to extract the shapes’ local-to-global features. On the other hand, the shapes are fed into a </span></span></span>convolutional neural network to generate multi-level features simultaneously. Then a hierarchical fusion network learns a general and unified representation from these two different types of features which capture multi-scale and multi-level properties of the underlying shapes. Extensive experiments and comprehensive comparisons demonstrate our HFN can achieve better performance in common shape analysis tasks, such as shape retrieval and recognition, and the learned hybrid descriptor is robust, informative, and discriminative with more potential for widespread applications.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"119 ","pages":"Article 101121"},"PeriodicalIF":1.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90028205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Graph-PBN: Graph-based parallel branch network for efficient point cloud learning 图- pbn:高效点云学习的基于图的并行分支网络

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-01-01 DOI: 10.1016/j.gmod.2021.101120

Cheng Zhang, Hao Chen, Haocheng Wan, Ping Yang, Zizhao Wu

{"title":"Graph-PBN: Graph-based parallel branch network for efficient point cloud learning","authors":"Cheng Zhang, Hao Chen, Haocheng Wan, Ping Yang, Zizhao Wu","doi":"10.1016/j.gmod.2021.101120","DOIUrl":"https://doi.org/10.1016/j.gmod.2021.101120","url":null,"abstract":"<div><p><span><span><span>In recent years, approaches based on graph convolutional networks (GCNs) have achieved state-of-the-art performance in point cloud learning. The typical pipeline of GCNs is modeled as a two-stage learning process: </span>graph construction and </span>feature learning<span>. We argue that such process exhibits low efficiency because a high percentage of the total time is consumed during the graph construction process when a large amount of sparse data are required to be accessed rather than on actual feature learning. To alleviate this problem, we propose a graph-based parallel branch network (Graph-PBN) that introduces a parallel branch structure to point cloud learning in this study. In particular, Graph-PBN is composed of two branches: the PointNet branch and the GCN branch. PointNet exhibits advantages in memory access and computational cost, while GCN behaves better in local context modeling. The two branches are combined in our architecture to utilize the potential of PointNet and GCN fully, facilitating the achievement of efficient and accurate recognition results. To better aggregate the features of each node in GCN, we investigate a novel operator, called EAGConv, to augment their local context by fully utilizing geometric and semantic features in a local graph. We conduct experiments on several benchmark datasets, and experiment results validate the significant performance of our method compared with other state-of-the-art approaches. Our code will be made publicly available at </span></span><span>https://github.com/zhangcheng828/Graph-PBN</span><svg><path></path></svg>.</p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"119 ","pages":"Article 101120"},"PeriodicalIF":1.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91725397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Authoring multi-style terrain with global-to-local control 使用全局到局部的控制来创建多样式的地形

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-01-01 DOI: 10.1016/j.gmod.2021.101122

Jian Zhang , Chen Li , Peichi Zhou , Changbo Wang , Gaoqi He , Hong Qin

{"title":"Authoring multi-style terrain with global-to-local control","authors":"Jian Zhang , Chen Li , Peichi Zhou , Changbo Wang , Gaoqi He , Hong Qin","doi":"10.1016/j.gmod.2021.101122","DOIUrl":"https://doi.org/10.1016/j.gmod.2021.101122","url":null,"abstract":"<div><p><span>The appearance styles of natural terrains vary significantly from region to region in real world, and there is a strong need to effectively produce realistic terrain with certain style in computer graphics<span><span>. In this paper, we advocate a novel neural network approach to the rapid synthesis of multi-style terrains that could directly learn and infer from real terrain data. The key idea is to explicitly devise a conditional </span>generative adversarial network (GAN) which encourages and favors the maximum-distance embedding of acquired styles in the latent space. Towards this functionality, we first collect a dataset that exhibits apparent terrain style diversity in their style attributes. Second, we design multiple </span></span>discriminators<span> that can distinguish different terrain styles. Third, we employ discriminators to extract terrain features in different spatial scales, so that the developed generator can produce new terrains by fusing the finer-scale and coarser-scale styles. In our experiments, we collect 10 typical terrain datasets from real terrain data that cover a wide range of regions. Our approach successfully generates realistic terrains with global-to-local style control. The experimental results have confirmed our neural network can produce natural terrains with high fidelity, which are user-friendly to style interpolation and style mixing for the terrain authoring task.</span></p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"119 ","pages":"Article 101122"},"PeriodicalIF":1.7,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91764503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4