Graphical Models最新文献_第8页

A data driven approach to generate realistic 3D tree barks 一个数据驱动的方法来生成现实的3D树树皮

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101166

Aishwarya Venkataramanan , Antoine Richard , Cédric Pradalier

引用次数: 0

ObjectFusion: Accurate object-level SLAM with neural object priors 目标融合:具有神经目标先验的精确目标级SLAM

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101165

Zi-Xin Zou , Shi-Sheng Huang , Tai-Jiang Mu , Yu-Ping Wang

{"title":"ObjectFusion: Accurate object-level SLAM with neural object priors","authors":"Zi-Xin Zou , Shi-Sheng Huang , Tai-Jiang Mu , Yu-Ping Wang","doi":"10.1016/j.gmod.2022.101165","DOIUrl":"10.1016/j.gmod.2022.101165","url":null,"abstract":"<div>Previous object-level Simultaneous Localization and Mapping (SLAM) approaches still fail to create high quality object-oriented 3D map in an efficient way. The main challenges come from how to represent the object shape effectively and how to apply such object representation to accurate online camera tracking efficiently. In this paper, we provide ObjectFusion as a novel object-level SLAM in static scenes which efficiently creates object-oriented 3D map with high-quality object reconstruction, by leveraging neural object priors. We propose a neural object representation with only a single encoder–decoder network to effectively express the object shape across various categories, which benefits high quality reconstruction of object instance. More importantly, we propose to convert such neural object representation as precise measurements to jointly optimize the object shape, object pose and camera pose for the final accurate 3D object reconstruction. With extensive evaluations on synthetic and real-world RGB-D datasets, we show that our ObjectFusion outperforms previous approaches, with better object reconstruction quality, using much less memory footprint, and in a more efficient way, especially at the object level.</div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"123 ","pages":"Article 101165"},"PeriodicalIF":1.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90153299","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

Construction of quasi-Bézier surfaces from boundary conditions 从边界条件构造拟bsamzier曲面

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101159

Yong-Xia Hao, Ting Li

引用次数: 0

A deep architecture for log-Euclidean Fisher vector end-to-end learning with application to 3D point cloud classification 一个深度架构的对数欧氏费雪向量端到端学习与应用于三维点云分类

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101164

Amira Chekir

{"title":"A deep architecture for log-Euclidean Fisher vector end-to-end learning with application to 3D point cloud classification","authors":"Amira Chekir","doi":"10.1016/j.gmod.2022.101164","DOIUrl":"https://doi.org/10.1016/j.gmod.2022.101164","url":null,"abstract":"<div>Point clouds are a widely used form of 3D data, which can be produced by depth sensors, such as RGB-D cameras. The classification of common elements of 3D point clouds remains an open research problem.We propose a new deep network approach for the end-to-end training of log-Euclidean Fisher vectors (LE-FVs), applied to the classification of 3D point clouds. Our method uses a log-Euclidean (LE) metric in order to extend the concept of Fisher vectors (FVs) to LE-FV encoding. The LE-FV was computed on covariance matrices of local 3D point cloud descriptors, representing multiple features. Our architecture is composed of two blocks. The first one aims to map the covariance matrices representing the 3D point cloud descriptors to the Euclidean space. The second block allows for joint and simultaneous learning of LE-FV Gaussian Mixture Model (GMM) parameters, LE-FV dimensionality reduction, and multi-label classification.Our LE-FV deep learning model is more accurate than the FV deep learning architecture. Additionally, the introduction of joint learning of 3D point cloud features in the log-Euclidean space, including LE-FV GMM parameters, LE-FV dimensionality reduction, and multi-label classification greatly improves the accuracy of classification. Our method has also been compared with the most popular methods in the literature for 3D point cloud classification, and it achieved good performance. The quantitative evidence will be shown through different experiments.</div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"123 ","pages":"Article 101164"},"PeriodicalIF":1.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91718989","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

Deep functional maps for simultaneously computing direct and symmetric correspondences of 3D shapes 用于同时计算三维形状的直接和对称对应的深度功能映射

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101163

Hui Wang , Bitao Ma , Junjie Cao , Xiuping Liu , Hui Huang

{"title":"Deep functional maps for simultaneously computing direct and symmetric correspondences of 3D shapes","authors":"Hui Wang , Bitao Ma , Junjie Cao , Xiuping Liu , Hui Huang","doi":"10.1016/j.gmod.2022.101163","DOIUrl":"https://doi.org/10.1016/j.gmod.2022.101163","url":null,"abstract":"<div>We introduce a novel method of isometric correspondences for 3D shapes, designed to address the problem of multiple solutions associated with deep functional maps when matching shapes with left-to-right reflectional intrinsic symmetries. Unlike the existing methods that only find the direct correspondences using single Siamese network, our proposed method is able to detect both the direct and symmetric correspondences among shapes simultaneously. Furthermore, our method detects the reflectional intrinsic symmetry of each shape. Key to our method is the using of two Siamese networks that learn consistent direct descriptors and their symmetric ones, combined with carefully designed regularized functional maps and supervised loss. This leads to the first deep functional map capable of both producing two high-quality correspondences of shapes and detecting the left-to-right reflectional intrinsic symmetry of each shape. Extensive experiments demonstrate that the proposed method obtains more accurate results than state-of-the-art methods for shape correspondences and reflectional intrinsic symmetries detection.</div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"123 ","pages":"Article 101163"},"PeriodicalIF":1.7,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91718990","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

Patch-based mesh inpainting via low rank recovery 基于补丁的网格绘制通过低等级恢复

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-07-01 DOI: 10.1016/j.gmod.2022.101139

Xiaoqun Wu, Xiaoyun Lin, Nan Li, Haisheng Li

引用次数: 2

Out-of-core outlier removal for large-scale indoor point clouds 大规模室内点云的核外离群值去除

IF 1.7 4区计算机科学

Graphical Models Pub Date : 2022-07-01 DOI: 10.1016/j.gmod.2022.101142

Linlin Ge, Jieqing Feng

引用次数: 0

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>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 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.</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>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 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 geometric details and an embedded 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.</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