共查询到20条相似文献，搜索用时 46 毫秒
1.
点云具有数据量大、无拓扑结构等特点,现有的深度学习语义分割模型难以充分挖掘大范围邻域内点云中所隐藏的几何特征。由此提出了一种基于空洞邻域并结合角度等几何特征作为模型输入的点云语义分割模型。首先,在局部邻域构建过程中,将图像处理的空洞卷积操作扩展至点云,建立空洞邻域结构,以扩大感受野;然后,在特征提取过程中,将中心点与邻域点之间相对坐标、距离、角度等基本几何特征作为模型输入,最大程度挖掘邻域内的几何特征;最后,基于所提邻域结构与特征提取算法构建了点云语义分割模型。采用Semantic3D数据集进行实验验证,结果表明,所提模型分割效果优于对比的点云语义分割算法,空洞邻域与局部几何输入特征能够有效改善点云语义模型的性能。 相似文献
2.
利用三维激光扫描技术测得的建筑物点云数据能够较清晰地表示建筑物的三维空间信息,提供高精度、高密度的建筑物表面描述。点云本身不直接显示自身所包含的特征信息,在进行局部形变提取时,需要进行点云分割工作。现有的应用于建(构)筑物的分割算法大多依赖于建(构)筑物特征设定突变阈值,当遇到复杂场景时,这些假设往往会导致错误。随着机器学习在点云处理领域的延伸,建(构)筑物点云数据边界的识别和分割有了新的实现思路。本文以某矿区工作面上方铁路桥两期三维激光扫描数据为例,采用神经网络方法对桥拱钢结构实行分割提取,在对1000万个标记桥梁点云数据进行训练后,神经网络模型可以学习操作人员识别点云中各点的属性并进行标记,并提取两期数据中的钢结构点云;对使用神经网络分割出的桥拱钢结构点云进行分析,通过对钢结构底边线进行特征线拟合、长度提取,计算钢结构的位移及拉伸量,并结合桥拱位移、形变量分析桥梁形变。研究表明:使用神经网络模型对标记数据进行训练可以有效识别建(构)筑物特征,并应用于建(构)筑物局部形变分析。 相似文献
3.
The extraction of object features from massive unstructured point clouds with different local densities, especially in the presence of random noisy points, is not a trivial task even if that feature is a planar surface. Segmentation is the most important step in the feature extraction process. In practice, most segmentation approaches use geometrical information to segment the 3D point cloud. The features generally include the position of each point (X, Y and Z), locally estimated surface normals and residuals of best fitting surfaces; however, these features could be affected by noisy points and in consequence directly affect the segmentation results. Therefore, massive unstructured and noisy point clouds also lead to bad segmentation (over-segmentation, under-segmentation or no segmentation). While the RANSAC (random sample consensus) algorithm is effective in the presence of noise and outliers, it has two significant disadvantages, namely, its efficiency and the fact that the plane detected by RANSAC may not necessarily belong to the same object surface; that is, spurious surfaces may appear, especially in the case of parallel-gradual planar surfaces such as stairs. The innovative idea proposed in this paper is a modification for the RANSAC algorithm called Seq-NV-RANSAC. This algorithm checks the normal vector (NV) between the existing point clouds and the hypothesised RANSAC plane, which is created by three random points, under an intuitive threshold value. After extracting the first plane, this process is repeated sequentially (Seq) and automatically, until no planar surfaces can be extracted from the remaining points under the existing threshold value. This prevents the extraction of spurious surfaces, brings an improvement in quality to the computed attributes and increases the degree of automation of surface extraction. Thus the best fit is achieved for the real existing surfaces. 相似文献
4.
针对目前将三维激光扫描技术应用于变形监测领域存在基准特征难以提取、点云数据分析缺乏适用的方法等问题，本文提出了一种基于点云法向量的基准特征提取与形变分析方法。首先利用局部平面拟合方法获得点云的法向量，并沿点云法矢方向探测基准点；然后利用三次B样条曲线对探测的正确基准点进行拟合；最后根据拟合曲线计算基准高程和对径点倾斜角分析基准特征形变信息。对某化工厂的罐体点云数据进行基准特征提取结果表明，该方法可以快速、全面地获取监测对象的整体信息，且能够正确分析监测对象的基准形变。 相似文献
5.
针对输电线点云数据中存在缺失、噪声等复杂环境,提出了一种基于模型残差聚类的激光点云电力线精细提取方法。首先根据归一化高程阈值分割去除近地面点,在此基础上,采用自适应维度特征和方向特征粗提取电力线点;然后以抛物线模型为约束条件,采用改进的建模方法,确定模型残差并对其进行密度聚类,根据聚类结果实现单根电力线精细提取;最后讨论了关键参数的选择对提取结果的影响。两景实测数据试验表明:该方法能快速实现点云部分缺失、噪声干扰等复杂环境下的电力线精细提取,无须电力线数目、点云密度等先验知识,对不同类型分裂导线提取均具有很好的适用性。单根电力线提取准确率达99.17%以上,模型误差最大值为0.167 m,中误差最大值为0.079 m。 相似文献
6.
本文基于高速公路高精度点云数据，首先通过点云数据的分类处理实现对树木点云数据的提取，将树木点云投影到水平面，采用DBSCAN密度聚类算法实现单根树木的提取；然后在数据密集区域存在树木树冠点云重叠的区域，本文结合树干几何特征提取树干的位置信息，计算所有点云到树干中心的欧氏距离，将所有点云归类到最近的树干进行粗分割；最后根据粗分割的树木轮廓特征确定树冠模型与树冠中心，提出了采用基于密度特征的格网竞争算法对重叠的区域进行精细分割。试验表明，本文采用的树木分割方法能够实现单棵树木精确提取。 相似文献
7.
结合城市车载激光扫描点云道路数据与地物点各自的特点和空间分布,用路面点云控制道路两侧地物点云进行滤波,从而实现道路路面点和地物点的分离,并根据城市道路中含有高出路面边缘的路缘石的结构特性设置阈值来提取路缘石,通过设置合适的格网及其邻域格网中点的密度特性进一步滤波路缘石,生成道路边线。 相似文献
8.
三维激光扫描技术是通过大量密集的扫描点,以模拟形式体现目标实体对象表面丰富的形体信息,不能获取的点云数据直接读取目标对象的矢量化信息,并进行点云数据处理。笔者在工作中,以EPS2008软件平台为基础,利用VBScript脚本进行二次开发,实现了点云数据分层提取、点云线状地物提取、点云特征轮廓提取、点云数据断面提取和点云高程提取等功能。本文针对已经实现的功能进行探讨,指出每种方法的特点和应用范围。 相似文献
9.
针对当前点云语义分割研究对地面站激光点云特征利用不足、正确率较低的问题,该文提出了一种基于多尺度球形邻域特征的深度神经网络算法。该算法基于多尺度球形邻域计算的地面激光点云的粗糙度、高斯曲率,以及全方差、线性度等基于协方差的多种特征,结合XYZ坐标、RGB颜色、激光反射强度组成47维特征向量作为神经网络的输入,经过多组参数组合实验优化神经网络结构,最后通过softmax分类器输出每个点的类别。利用Semantic-3D测试集验证所提的深度神经网络模型,取得了较好的分类精度,总体正确率和平均交并比分别达到了86.6%和55.0%。实验结果表明,所提算法充分利用了地面站激光点云的特征,可有效提升语义分割的正确率。 相似文献
10.
大型水工建筑物和城市地下轨道是国家的重要基础设施，人工变形监测耗时费力，利用激光雷达扫描对其进行自动化的变形监测已成为研究热点。而此类建筑物具有较多的平面结构特征，为了高效去除平面结构扫描点云中的噪声点，本文提出了一种融合主成分分析和局部邻域的噪声剔除方法，首先利用主成分分析剔除全局噪声，然后利用局部邻域对局部小噪声进行剔除，结合实例对该方法进行了验证。应用结果表明，该方法效率高、算法简单可行，具有较高的推广应用价值。 相似文献
11.
Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology. However, it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points. The paper analyzes the characteristics of Laplacian of Gaussian (LoG) Filter and its potential use for structure detection in LiDAR data. A feature detection method based on LoG filtering is presented and experimented on the unstructured points. The method filters the elevation value (namely, z coordinate value) of each point by convolution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground objects/features. The experiments are carried on a point cloud dataset acquired from a neighborhood area. The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size, which specifies the local area that is analyzed. 相似文献
12.
利用树干石灰涂层在激光点云中的高反射特性,本文提出一种基于点云强度的公园乔木检测方法。首先,使用三维激光扫描仪采集点云,并对点云进行去噪以及地面滤波。接着,对非地面点进行阈值分割,高强度部分包含了涂有石灰的树干。最后,对高强度点实施欧式聚类算法,通过尺寸与点数的约束,得到树干的聚类单元。通过实验证明,该方法具有自动化程度高、检测率高、误检率低等优势,满足公园调查的技术要求。 相似文献
13.
欧氏聚类算法是多元统计中的一种重要分类方法,可以将其应用于测绘领域中点云数据的分割。本文首先计算点云数据中两点之间的欧氏距离,将距离小于指定阈值作为分为一类的判定准则;然后迭代计算,直至所有的类间距大于指定阈值,完成欧氏聚类分割。具体步骤为：①利用Octree法建立点云数据拓扑组织结构;②对每个点进行k近邻搜索,计算该点与k个邻近点之间的欧氏距离,最小归为一类;③设置一定的阈值,对步骤②迭代计算,直至所有类与类之间的距离大于指定阈值。试验证明,欧氏聚类算法对不同测量技术手段获取的点云数据均具有适用性,可以成功对点云数据进行分割,分割效果良好。 相似文献
14.
Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points.The paper analyzes the characteristics of Laplacian of Gaussian(LoG) Filter and its potential use for structure detection in LiDAR data.A feature detection method based on LoG filtering is presented and ex-perimented on the unstructured points.The method filters the elevation value(namely,z coordinate value) of each point by convo-lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob-jects/features.The experiments are carried on a point cloud dataset acquired from a neighborhood area.The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size,which specifies the local area that is analyzed. 相似文献
15.
点云数据分割是点云数据处理的主要工作,也是实现地物自动识别的前提和关键环节,由于各种原因,目前点云数据分割自动化程度不高,尚需进一步的深入研究。本文以机载云数据为研究对象,提出了基于密度聚类方法的激光点云数据分割方法,该方法具有速度快、分割效果好、适应性强等优势,为后续的地物自动识别奠定了基础。 相似文献
16.
基于点云分类常用的近邻聚类法和物体表面分割等方法,本文提出了一种基于最大网格密度的近邻聚类的方法。该方法首先对原始点云进行低点提取,设置格网的大小,在此基础上对点云数据进行去噪并进行主成分分析,再对点云空间进行均匀格网化,使具有最大密度的格网为聚类中心,加入高程、强度以及法向量等特征对分割后的点云实现了不同地物的分类,提高了运算效率,降低了错分率。 相似文献
17.
以激光点云数据和倾斜多视影像为研究对象，提出了一种结合机载点云、地面点云及倾斜多视纹理的融合多源特征的建筑物三维模型重建方法。该方法结合点云面元以及影像边界特征，利用倾斜影像的线特征对顶面及立面模型进行边界规则约束，实现了面元自动拓扑重建；通过交互编辑完成不同复杂程度的建筑模型重建，并对模型进行纹理映射。实验结果表明，该方法能够有效提升城市建筑物三维模型重建的效率和边界精度，为利用多源数据的空地联合建筑物三维精细重建提供了一套切实可行的解决方案。 相似文献
18.
This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks. Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods. 相似文献
19.
针对现有大规模点云数据平面特征分割方法中存在的错误识别、效率低、抗噪性差等问题,该文提出一种基于2D霍夫变换和八叉树的建筑物平面精细分割方法。该方法首先,对原始点云进行空间均匀降采样并向X-Y面投影,利用改进的2D霍夫变换算法提取投影后的点云线段,使用选权迭代法精确计算线段所在直线的方程及端点坐标,进一步确定立面的空间几何方程;接下来,建立原始点云数据的八叉树结构,利用端点坐标设计立方体并分割出立方体内的立面点云;最后,将立面点云从原始点云中剔除,对余下点云降采样并向X-Z面投影,重复以上过程分割水平面点云。试验验证了该文方法对建筑物面状特征分割的有效性。 相似文献