基于Z-buffer算法优化的大型变电站场景模型快速线消隐方法

针对现有三维模型消隐方法面向大规模三维场景模型应用中存在的计算复杂、耗时长等缺陷, 本文提出了基于改进 Z-buffer 算法对大型变电站场景消隐的快速可视化方法。首先,为了简化计算,将场景模 型数据整合并重构;其次,通过透视投影变换将变电场景模型像素化;进一步,基于 Z-buffer 算法高效的像素 化计算特性提出了快速模型筛选方法,从而得到变电场景的子模型遮挡关系。最后,实验中将所得遮挡关系列 表融合现有消隐算法,结果表明本文提出的方法能够大幅度提升消隐的运算性能。     

大规模场景分割及LOD结构生成算法研究

在三维渲染中,场景的复杂程度是限制三维引擎渲染能力的主要原因。借助GIS系统中常用的对地图进行分片和分层的思想,对大规模三维场景进行处理,建立起多层次分片的LOD结构。具体步骤主要包括对三维模型的简化和分割。简化主要采用传统的边塌陷方法以及适当的局部细节剔除策略;分割指基于场景的空间位置信息,将边界区的三角面片进行切分,使一个场景结构分裂成两个场景结构。     

大规模三维场景几何信号基本处理方法

提出了基于激光扫描数据的大规模场景重建的方法,通过三维数据分割、提取结构特征、计算虚特征、简化数据,为配准提供可靠基元．根据特征单元和最小生成树原则,拼合多机位三维数据．最后,建立三维场景网格模型,给出空洞填补方法．实验证明了该算法的鲁棒性．     

视相关大规模三维地形实时绘制技术综述

大规模三维地形表面实时绘制一直是国内外的研究热点.综述了大规模三维地形表面实时绘制算法的分类,包括基于规则网格和非规则网格、基于in-core和out-of-core、基于CPU和GPU的三大类绘制算法;阐述了大规模三维地形表面实时绘制算法的发展现状和关键技术,通过out-of-core算法的数据预取策略解决大数据量模型的交互式渲染问题,采用视点相关的多分辨率技术简化整个场景的复杂度,减少绘制的数据量;指出了各种方法的优点与不足,最后对大规模三维地形表面实时绘制所需要研究的问题进行了总结.     

LOD模型生成算法研究

细节层次模型(LOD)是指对同一个场景或场景中的物体,使用具有不同细节的方法得到一组模型,供绘制时使用.建立LOD模型能很有效地降低数据量和复杂度,实现三维场景的实时处理.文中通过对LOD模型生成算法进行分析,研究了基于点删除算法的多分辨率模型生成方法、递进网格的简化算法、基于二次误差度量的几何简化算法等三种算法的实现步骤和误差计算方法.为以后在地形可视化、虚拟现实等领域的应用提供了理论依据.     

基于径向基函数的三维网格鲁棒公钥非盲水印

针对径向基函数水印算法不能抵抗剪切和伪造攻击以及鲁棒性不足等问题,提出一种大规模三维网格鲁棒公钥非盲谱水印算法.首先引入椭圆曲线数字签名算法以抵抗模型伪造;然后提出一种基于二次误差简化的中心点集优化选取算法来降低预基函数的奇异性,提高网格重构质量;为了提高算法的鲁棒性并抵抗剪切攻击,对大规模三维网格进行语义分片,引入嵌...     

LOD模型生成算法研究

细节层次模型(LOD)是指对同一个场景或场景中的物体，使用具有不同细节的方法得到一组模型，供绘制时使用。建立LOD模型能很有效地降低数据量和复杂度，实现三维场景的实时处理。文中通过对LOD模型生成算法进行分析，研究了基于点删除算法的多分辨率模型生成方法、递进网格的简化算法、基于二次误差度量的几何简化算法等三种算法的实现步骤和误差计算方法。为以后在地形可视化、虚拟现实等领域的应用提供了理论依据。     

水平集网格简化算法在遗址场景中的应用

将水平集方法引入到三维模型网格简化中,构造符号距离函数,函数的零集定义为初始曲面;引入一个能量泛涵,通过对其极小化诱导出一个水平集形式的二阶几何偏微分方程,从而将网格简化过程转化为隐式模型的体素扩散过程。该方法目前已经用于文化遗产数字化的大场景和文物的模型简化中。对水平集网格简化算法和现常用的基于点对收缩的网格简化算法在视觉质量和几何误差方面做了比较和分析,实验表明该方法适用于任意拓扑形状的网格模型,使得模型大规模简化后,在保持较低误差的同时,仍然能够保持相当多的重要几何特征和较好的整体视觉效果。     

基于改进四叉树分割和结点存储的LOD算法

多层次细节（LOD）算法作为目前使用最多的地形数据简化算法,对提升渲染速度加快场景可视化有着重要作用,而其中以基于四叉树的LOD算法应用最为广泛。通过对以往算法的研究,提出一种对四叉树的分割和结点存储结构同时进行改进的LOD算法。该算法通过减少误差判断次数加快了四叉树的生成速度,同时改变传统的结点存储方式,降低了数据的冗余存储。     

基于点采样的大规模三维模型显示

大规模三维模型的实时显示一直是许多虚拟现实应用追求的目标之一,也是计算机图形学领域的研究热点之一。文中采用基于采样点的显示方法,把具有显示质量优势的传统三角形与显示速度快、简化表示能力强的采样点,通过八叉树有机组织起来;通过对基本算法做出有效的改进,并添加计算分解和重用、有效的数据管理等模块,以高效的实现方式完成了30,000个独立运动的虚拟人场景的实时显示。文中的方法也可处理其它场景,能直接应用于紧急疏散或影视、游戏等领域中大规模三维模型的显示,具有很好的应用前景。     

结合语义分割与模型匹配的室内场景重建方法

目的 由于室内点云场景中物体的密集性、复杂性以及多遮挡等带来的数据不完整和多噪声问题,极大地限制了室内点云场景的重建工作,无法保证场景重建的准确度。为了更好地从无序点云中恢复出完整的场景,提出了一种基于语义分割的室内场景重建方法。方法 通过体素滤波对原始数据进行下采样,计算场景三维尺度不变特征变换（3D scale-invariant feature transform,3D SIFT）特征点,融合下采样结果与场景特征点从而获得优化的场景下采样结果;利用随机抽样一致算法（random sample consensus,RANSAC）对融合采样后的场景提取平面特征,将该特征输入PointNet网络中进行训练,确保共面的点具有相同的局部特征,从而得到每个点在数据集中各个类别的置信度,在此基础上,提出了一种基于投影的区域生长优化方法,聚合语义分割结果中同一物体的点,获得更精细的分割结果;将场景物体的分割结果划分为内环境元素或外环境元素,分别采用模型匹配的方法、平面拟合的方法从而实现场景的重建。结果 在S3DIS （Stanford large-scale 3D indoor space dataset）数据集上进行实验,本文融合采样算法对后续方法的效率和效果有着不同程度的提高,采样后平面提取算法的运行时间仅为采样前的15%;而语义分割方法在全局准确率（overall accuracy,OA）和平均交并比（mean intersection over union,mIoU）两个方面比PointNet网络分别提高了2.3%和4.2%。结论 本文方法能够在保留关键点的同时提高计算效率,在分割准确率方面也有着明显提升,同时可以得到高质量的重建结果。     

Exploration trees on highly complex scenes: A new approach for 3D segmentation

A new strategy for automatic object extraction in highly complex scenes is presented in this paper. The method proposed gives a solution for 3D segmentation avoiding most restrictions imposed in other techniques. Thus, our technique is applicable on unstructured 3D information (i.e. cloud of points), with a single view of the scene, scenes consisting of several objects where contact, occlusion and shadows are allowed, objects with uniform intensity/texture and without restrictions of shape, pose or location. In order to have a fast segmentation stopping criteria, the number of objects in the scene is taken as input. The method is based on a new distributed segmentation technique that explores the 3D data by establishing a set of suitable observation directions. For each exploration viewpoint, a strategy [3D data]-[2D projected data]-[2D segmentation]-[3D segmented data] is accomplished. It can be said that this strategy is different from current 3D segmentation strategies. This method has been successfully tested in our lab on a set of real complex scenes. The results of these experiments, conclusions and future improvements are also shown in the paper.     

Automatic 3D Indoor Scene Updating with RGBD Cameras

Since indoor scenes are frequently changed in daily life, such as re‐layout of furniture, the 3D reconstructions for them should be flexible and easy to update. We present an automatic 3D scene update algorithm to indoor scenes by capturing scene variation with RGBD cameras. We assume an initial scene has been reconstructed in advance in manual or other semi‐automatic way before the change, and automatically update the reconstruction according to the newly captured RGBD images of the real scene update. It starts with an automatic segmentation process without manual interaction, which benefits from accurate labeling training from the initial 3D scene. After the segmentation, objects captured by RGBD camera are extracted to form a local updated scene. We formulate an optimization problem to compare to the initial scene to locate moved objects. The moved objects are then integrated with static objects in the initial scene to generate a new 3D scene. We demonstrate the efficiency and robustness of our approach by updating the 3D scene of several real‐world scenes.     

多特征融合与残差优化的点云语义分割方法

目的 当前的大场景3维点云语义分割方法一般是将大规模点云切成点云块再进行处理。然而在实际计算过程中,切割边界的几何特征容易被破坏,使得分割结果呈现明显的边界现象。因此,迫切需要以原始点云作为输入的高效深度学习网络模型,用于点云的语义分割。方法 为了解决该问题,提出基于多特征融合与残差优化的点云语义分割方法。网络通过一个多特征提取模块来提取每个点的几何结构特征以及语义特征,通过对特征的加权获取特征集合。在此基础上,引入注意力机制优化特征集合,构建特征聚合模块,聚合点云中最具辨别力的特征。最后在特征聚合模块中添加残差块,优化网络训练。最终网络的输出是每个点在数据集中各个类别的置信度。结果 本文提出的残差网络模型在S3DIS （Stanford Large-scale 3D Indoor Spaces Dataset）与户外场景点云分割数据集Semantic3D等2个数据集上与当前的主流算法进行了分割精度的对比。在S3DIS数据集中,本文算法在全局准确率以及平均准确率上均取得了较高精度,分别为87.2%,81.7%。在Semantic3D数据集上,本文算法在全局准确率和平均交并比上均取得了较高精度,分别为93.5%,74.0%,比GACNet （graph attention convolution network）分别高1.6%,3.2%。结论 实验结果验证了本文提出的残差优化网络在大规模点云语义分割的应用中,可以缓解深层次特征提取过程中梯度消失和网络过拟合现象并保持良好的分割性能。     

Unsupervised segmentation of unknown objects in complex environments

This paper presents a novel object segmentation approach for highly complex indoor scenes. Our approach starts with a novel algorithm which partitions the scene into distinct regions whose boundaries accurately conform to the physical object boundaries in the scene. Next, we propose a novel perceptual grouping algorithm based on local cues (e.g., 3D proximity, co-planarity, and shape convexity) to merge these regions into object hypotheses. Our extensive experimental evaluations demonstrate that our object segmentation results are superior compared to the state-of-the-art methods.     

基于主要遮挡物的动态可见性算法

对景物密集的复杂场景 ,提出了基于主要遮挡物的动态可见性算法 .该算法通过场景中预先定义的主要遮挡物 ,动态地形成一个遮挡树 ,位于遮挡树遮挡区域中的景物将被剔除 .当场景按照 BSP树组织 ,并按从前向后的顺序绘制场景时 ,算法具有高效率 .对主要遮挡物采用简化的遮挡物代理 ,对盒子类型的遮挡物提出了一种有效的简化算法 .该算法已经被“RTG三维图形开发工具包”采用 ,经实际验证 ,对复杂场景 ,该算法可以明显地提高绘制速度     

Integrating Automated Range Registration with Multiview Geometry for the Photorealistic Modeling of Large-Scale Scenes

The photorealistic modeling of large-scale scenes, such as urban structures, requires a fusion of range sensing technology and traditional digital photography. This paper presents a system that integrates automated 3D-to-3D and 2D-to-3D registration techniques, with multiview geometry for the photorealistic modeling of urban scenes. The 3D range scans are registered using our automated 3D-to-3D registration method that matches 3D features (linear or circular) in the range images. A subset of the 2D photographs are then aligned with the 3D model using our automated 2D-to-3D registration algorithm that matches linear features between the range scans and the photographs. Finally, the 2D photographs are used to generate a second 3D model of the scene that consists of a sparse 3D point cloud, produced by applying a multiview geometry (structure-from-motion) algorithm directly on a sequence of 2D photographs. The last part of this paper introduces a novel algorithm for automatically recovering the rotation, scale, and translation that best aligns the dense and sparse models. This alignment is necessary to enable the photographs to be optimally texture mapped onto the dense model. The contribution of this work is that it merges the benefits of multiview geometry with automated registration of 3D range scans to produce photorealistic models with minimal human interaction. We present results from experiments in large-scale urban scenes.     

3D scene analysis from a single range image through occlusion graphs

This paper presents a new strategy to extract knowledge about the objects and their relative location in a complex scene when a single range image is taken. The analysis process is based on a range data distributed segmentation technique, which separates the components of the scene, and on a silhouette segmentation method, which classified the silhouette in real (non occluded) and false (occluded) parts. Finally, an occlusion graph provides a compact representation about the layout and relationship of the objects in the scene. This information is essential before higher level tasks in complex scenes – like recognition, understanding and robot interaction – are carried out. An extensive experimentation has been accomplished under real conditions in scenes of up to 12 objects yielding a very good performance. The experiments and results carried out validate the goodness of this approach in 3D environments.     

一种改进的射线跟踪路径搜索算法

基于几何光学原理的射线跟踪信道建模方法能够对场强、时延、到达角等多径信息进行精确预测,该方法多用多边形或三角构建三维场景模型。为了提高射线跟踪的路径搜索效率,提出一种改进的三角化射线跟踪路径搜索算法。在原带符号体积法中加入对实际不相交三角形的排除过程,简化判断过程,减小计算量。以三个不同类型的场景模型为例,对改进前后的算法进行仿真比较。结果表明,改进的路径搜索算法比原算法运算速度更快,且场景模型越复杂,效率提高越明显。