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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101166
Aishwarya Venkataramanan , Antoine Richard , Cédric Pradalier

3D models of trees are ubiquitous in video games, movies, and simulators. It is of paramount importance to generate high quality 3D models to enhance the visual content, and increase the diversity of the available models. In this work, we propose a methodology to create realistic 3D models of tree barks from a consumer-grade hand-held camera. Additionally, we present a pipeline that makes use of multi-view 3D Reconstruction and Generative Adversarial Networks (GANs) to generate the 3D models of the barks. We introduce a GAN referred to as the Depth-Reinforced-SPADE to generate the surfaces of the tree barks and the bark color concurrently. This GAN gives extensive control on what is being generated on the bark: moss, lichen, scars, etc. Finally, by testing our pipeline on different Northern-European trees whose barks exhibit radically different color patterns and surfaces, we show that our pipeline can be used to generate a broad panel of tree species’ bark.

树木的3D模型在电子游戏、电影和模拟器中无处不在。生成高质量的3D模型对于增强视觉内容和增加可用模型的多样性至关重要。在这项工作中,我们提出了一种从消费级手持相机创建逼真的树皮3D模型的方法。此外,我们提出了一个管道,利用多视图3D重建和生成对抗网络(gan)来生成银行的3D模型。我们引入了一种称为深度增强- spade的GAN来同时生成树皮表面和树皮颜色。这种GAN对树皮上产生的东西进行了广泛的控制:苔藓、地衣、疤痕等。最后,通过在不同的北欧树木上测试我们的管道,这些树木的树皮呈现出截然不同的颜色图案和表面,我们表明我们的管道可以用来生成一个广泛的树种树皮面板。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101165
Zi-Xin Zou , Shi-Sheng Huang , Tai-Jiang Mu , Yu-Ping Wang

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.

以往的对象级同步定位与映射(SLAM)方法仍然不能有效地生成高质量的面向对象三维地图。主要的挑战是如何有效地表示物体形状,以及如何将这种物体表示有效地应用于准确的在线摄像机跟踪。在本文中,我们提供了ObjectFusion作为静态场景中一种新的对象级SLAM,通过利用神经对象先验,有效地创建具有高质量对象重建的面向对象3D地图。我们提出了一种仅使用单个编码器-解码器网络的神经对象表示方法,可以有效地表达不同类别的对象形状,从而有利于高质量的对象实例重建。更重要的是,我们提出将这种神经对象表示转换为精确测量,共同优化物体形状、物体姿态和相机姿态,以最终精确地重建三维物体。通过对合成和真实世界RGB-D数据集的广泛评估,我们表明我们的ObjectFusion优于以前的方法,具有更好的对象重建质量,使用更少的内存占用,并且以更有效的方式,特别是在对象级别。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101159
Yong-Xia Hao, Ting Li

The quasi-Bézier surface is a kind of commonly used surfaces in CAGD/CAD systems. In this paper, we present a novel approach to construct quasi-Bézier surfaces from the boundary information based on a general second order functional. This functional includes many common functionals as special cases, such as the Dirichlet functional, the biharmonic functional and the quasi-harmonic functional etc. The problem turns into solving simple linear equations about inner control points, and finally the internal control points of the resulting quasi-Bézier surface can be obtained as linear combinations of the given boundary control points. Some representative examples show the effectiveness of the presented method.

准bsamizier曲面是CAGD/CAD系统中常用的一类曲面。本文提出了一种利用一般二阶泛函的边界信息构造拟bsamzier曲面的新方法。该泛函包含了狄利克雷泛函、双调和泛函和拟调和泛函等常见泛函的特例。将问题转化为求解关于内控制点的简单线性方程,最终得到拟bsamzier曲面的内控制点作为给定边界控制点的线性组合。算例表明了所提方法的有效性。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101164
Amira Chekir

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.

点云是一种广泛使用的3D数据形式,它可以由深度传感器产生,如RGB-D相机。三维点云的公共元素分类仍然是一个开放的研究问题。我们提出了一种新的深度网络方法,用于log-Euclidean Fisher向量(LE-FVs)的端到端训练,并应用于3D点云的分类。我们的方法使用对数欧几里得(LE)度量,以便将Fisher向量(fv)的概念扩展到LE- fv编码。在局部三维点云描述符的协方差矩阵上计算LE-FV,表示多个特征。我们的建筑由两个街区组成。第一个是将表示三维点云描述符的协方差矩阵映射到欧几里德空间。第二个块允许联合和同时学习LE-FV高斯混合模型(GMM)参数,LE-FV降维和多标签分类。我们的LE-FV深度学习模型比FV深度学习架构更准确。此外,引入对数欧氏空间三维点云特征的联合学习,包括LE-FV GMM参数、LE-FV降维、多标签分类等,大大提高了分类精度。我们的方法还与文献中最流行的三维点云分类方法进行了比较,取得了良好的性能。定量证据将通过不同的实验来展示。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-09-01 DOI: 10.1016/j.gmod.2022.101163
Hui Wang , Bitao Ma , Junjie Cao , Xiuping Liu , Hui Huang

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.

我们介绍了一种新的三维形状等距对应方法,旨在解决与深度功能映射相关的多重解问题,当与左到右反射固有对称性匹配形状时。不同于现有方法仅使用单个Siamese网络找到直接对应,我们提出的方法能够同时检测形状之间的直接对应和对称对应。此外,我们的方法检测每个形状的反射固有对称性。我们方法的关键是使用两个Siamese网络来学习一致的直接描述符及其对称描述符,并结合精心设计的正则化功能映射和监督损失。这导致了第一个深度功能图,既能产生两个高质量的形状对应,又能检测每个形状的从左到右反射的内在对称性。大量的实验表明,该方法比现有的形状对应和反射本征对称性检测方法获得了更准确的结果。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-07-01 DOI: 10.1016/j.gmod.2022.101139
Xiaoqun Wu, Xiaoyun Lin, Nan Li, Haisheng Li

Mesh inpainting aims to fill the holes or missing regions from observed incomplete meshes and keep consistent with prior knowledge. Inspired by the success of low rank in describing similarity, we formulate the mesh inpainting problem as the low rank matrix recovery problem and present a patch-based mesh inpainting algorithm. Normal patch covariance is adapted to describe the similarity between surface patches. By analyzing the similarity of patches, the most similar patches are packed into a matrix with low rank structure. An iterative diffusion strategy is first designed to recover the patch vertex normals gradually. Then, the normals are refined by low rank approximation to keep the overall consistency and vertex positions are finally updated. We conduct several experiments in different 3D models to verify the proposed approach. Compared with existing algorithms, our experimental results demonstrate the superiority of our approach both visually and quantitatively in recovering the mesh with self-similarity patterns.

网格补绘的目的是填补观察到的不完整网格中的空洞或缺失区域,并保持与先验知识的一致性。受低秩描述相似度成功的启发,我们将网格绘制问题表述为低秩矩阵恢复问题,并提出了一种基于patch的网格绘制算法。采用正态斑块协方差来描述表面斑块之间的相似性。通过分析patch的相似度,将最相似的patch打包成一个低阶结构的矩阵。首先设计迭代扩散策略,逐步恢复patch顶点法线;然后,通过低秩近似法对法线进行细化以保持整体一致性,最后更新顶点位置。我们在不同的3D模型中进行了几个实验来验证所提出的方法。与现有算法相比,我们的实验结果表明,我们的方法在视觉上和定量上都具有自相似模式的网格恢复的优势。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-07-01 DOI: 10.1016/j.gmod.2022.101142
Linlin Ge, Jieqing Feng

An accurate coarse-to-fine out-of-core outlier removal method is proposed for large-scale indoor point clouds by mining the geometric shape constraints. In coarse processing stage, a low-resolution point cloud (LPC) is obtained using random downsampling. LPC has the same density distribution as the raw point clouds (RPC), which is important information for outlier removal. The correspondences from the LPC to the RPC are also recorded. The outliers in the LPC are removed via a global threshold. The outliers in the RPC are roughly removed guided by the cleaned LPC. In refinement processing stage, the cleaned LPC is segmented into planar and non-planar segments; and the LPC segmentation is transferred to the RPC. Finally, the outliers in each RPC segment are removed elaborately via a local threshold by exploring the shape information. The experiments show that the proposed method improves the quality of outlier removal results.

通过挖掘几何形状约束,提出了一种针对室内大尺度点云的精确粗到精的核心外离群点去除方法。在粗处理阶段,采用随机下采样获得低分辨率点云。LPC具有与原始点云(RPC)相同的密度分布,这是去除离群值的重要信息。从LPC到RPC的通信也被记录下来。LPC中的异常值通过全局阈值去除。在清理后的LPC的引导下,RPC中的异常值被粗略地移除。在精加工阶段,将清洗后的LPC分割为平面段和非平面段;将LPC分段传送给RPC。最后,通过探索形状信息,通过局部阈值精细地去除每个RPC段中的异常值。实验表明,该方法提高了异常值去除结果的质量。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-05-01 DOI: 10.1016/j.gmod.2022.101136
Qing Huang, Wen-Xiang Zhang, Qi Wang, Ligang Liu, Xiao-Ming Fu

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.

我们提出了一种新的方法来解开和优化所有的六边形网格。该算法的核心是自适应边界优化过程,该过程显著提高了实际鲁棒性。给定具有许多倒六面体单元的全六边形网格,我们首先优化对输入边界具有较小近似误差的高质量四边形边界网格。由于边界约束限制了搜索无反转网格的优化空间,因此我们放松边界约束以生成无反转的全十六进制网格。我们开发了一种自适应边界松弛算法,以隐含地限制松弛边界和输入边界之间的形状差异,从而促进下一步工作。最后,开发了一种自适应边界差最小化方法,以有效地迫使第一步的松弛边界和优化边界之间的距离差接近零,同时避免倒置元素。我们在包含1004个全六边形网格的数据集上展示了我们的算法的有效性。与以前的方法相比,我们的方法具有更高的实用鲁棒性。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-05-01 DOI: 10.1016/j.gmod.2022.101138
Yeying Fan , Qian Ma , Guangshun Wei , Zhiming Cui , Yuanfeng Zhou , Wenping Wang

The tooth axes, defined on 3D tooth model, play a key role in digital orthodontics, which is usually used as an important reference in automatic tooth arrangement and anomaly detection. In this paper, we propose an automatic deep learning network (TAD-Net) of tooth axis detection based on rotation transformation encoding. By utilizing quaternion transformation, we convert the geometric rotation transformation of the tooth axes into the feature encoding of the point cloud of 3D tooth models. Furthermore, the feature confidence-aware attention mechanism is adopted to generate dynamic weights for the features of each point to improve the network learning accuracy. Experimental results show that the proposed method has achieved higher detection accuracy on the constructed dental data set compared with the existing networks.

在三维牙齿模型上定义的牙轴在数字正畸中起着关键作用,通常作为自动排牙和异常检测的重要参考。本文提出了一种基于旋转变换编码的齿轴检测自动深度学习网络(TAD-Net)。利用四元数变换,将齿轴的几何旋转变换转化为三维齿模型点云的特征编码。进一步,采用特征置信度感知注意机制,对每个点的特征生成动态权值,提高网络的学习精度。实验结果表明,与现有网络相比,该方法在构建的牙齿数据集上取得了更高的检测精度。
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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 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-05-01 DOI: 10.1016/j.gmod.2022.101135
Han Chen, Minghai Chen, Lin Lu

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.

个性化小雕像的设计和制造的最新进展使普通用户可以通过3D打印技术创造出高质量的小雕像。头发在获得雕像的真实感方面起着重要作用。现有的毛发重建方法存在对采集设备的高需求,或者结果非常粗略地近似。我们提出了一种新的表面重建方法,从发丝级别的发型中生成具有几何特征的3D可打印头发模型,从而将现有的数字头发数据库转换为3D可打印数据库,而不是通过扫描设备为雕像创建头发。给定一个发束级别的头发模型,我们通过发束聚类过滤发束,保留主要特征,并分两个阶段重建发束。首先,我们的算法是根据头发的结构提取头发轮廓表面并计算每个顶点的法线的关键。接下来,通过泊松曲面重建,获得了一个具有几何细节和嵌入方向场的闭合流形三角形网格。我们在没有用户互动的情况下获得了封闭的多种发型,有利于个性化的雕像制作。我们通过展示大量的例子来验证我们的方法的可行性。
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引用次数: 0
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