Efficient and automatic plane detection approach for 3-D rock mass point clouds

The detection of planar regions from three-dimensional (3-D) laser scanning point clouds has become more and more significant in many scientific fields, including 3-D reconstruction, augmented reality and analysis of discontinuities. In rock engineering, planes extracted from rock mass point clouds are the foundational step to build 3-D numerical models of rock mass, which is significant in analysis of rock stability. In the past, several approaches have been proposed for detecting planes from TLS point clouds. However, these methods have difficulties in processing rock points because of the uniqueness of rock. This paper introduces a novel and efficient method for plane detection from 3-D rock mass point clouds. Firstly, after filtering the raw point clouds of rock mass acquired through laser scanning, the point cloud is split into some small voxels according to the specified resolution. Then, for the purpose of acquisition of high-quality growth units, an accurate coplanarity test process is used in each voxel. Meanwhile, the accurate neighborhood information can be built according to the result of coplanarity test. Finally, small voxels are clustered into a completed plane by region growing and the procedure of postprecessing. The performance of this method was tested in one icosahedron point cloud and three rock mass point clouds. Compared with the existing methods, the results demonstrate superior performance of our method in the field of plane detection.

     

Robust normal estimation and region growing segmentation of infrastructure 3D point cloud models

Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, maintenance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time consuming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reliable normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmentation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.     

Improved Iterative Closest Point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration

ABSTRACT

Aiming at the problem of long computation time and poor registration accuracy in the current three-dimensional point cloud registration problem, this paper presents a k-dimensional Tree(KD-tree) improved ICP algorithm(KD-tree_ICP) that combines point cloud filtering and adaptive fireworks algorithms for coarse registration. On the basis of the typical KD-tree improved ICP algorithm, the point cloud filtering process and adaptive firework coarse registration process are added. Firstly, the point cloud data acquired by the 3D laser scanner is filtered. And then the adaptive fireworks algorithm is used to perform coarse registration on the filtered point cloud data. Next, the KD-tree_ICP algorithm is used to perform accurate registration on the basis of coarse registration, and the obtained translation and rotation relations are applied to the original point cloud data to obtain the result after registration. Finally, 3D point clouds of physical models of five statues are used for experimental verification, including error analysis, stability analysis and comparison with other algorithms. The experimental results show that the method proposed in this paper has greatly improved the calculation speed and accuracy, and the algorithm is stable and reliable, which can also be applied to the reconstruction of 3D building models, restoration of cultural relics, precision machining and other fields.     

Skeleton-based intrinsic symmetry detection on point clouds

We present a skeleton-based algorithm for intrinsic symmetry detection on imperfect 3D point cloud data. The data imperfections such as noise and incompleteness make it difficult to reliably compute geodesic distances, which play essential roles in existing intrinsic symmetry detection algorithms. In this paper, we leverage recent advances in curve skeleton extraction from point clouds for symmetry detection. Our method exploits the properties of curve skeletons, such as homotopy to the input shape, approximate isometry-invariance, and skeleton-to-surface mapping, for the detection task. Starting from a curve skeleton extracted from an input point cloud, we first compute symmetry electors, each of which is composed of a set of skeleton node pairs pruned with a cascade of symmetry filters. The electors are used to vote for symmetric node pairs indicating the symmetry map on the skeleton. A symmetry correspondence matrix (SCM) is constructed for the input point cloud through transferring the symmetry map from skeleton to point cloud. The final symmetry regions on the point cloud are detected via spectral analysis over the SCM. Experiments on raw point clouds, captured by a 3D scanner or the Microsoft Kinect, demonstrate the robustness of our algorithm. We also apply our method to repair incomplete scans based on the detected intrinsic symmetries.     

基于自注意力机制的点云分类分割

3D点云由于其无序性以及缺少拓扑信息使得点云的分类与分割仍具有挑战性.针对上述问题,我们设计了一种基于自注意力机制的3D点云分类算法,可学习点云的特征信息,用于目标分类与分割.首先,设计适用于点云的自注意力模块,用于点云的特征提取.通过构建领域图来加强输入嵌入,使用自注意力机制进行局部特征的提取与聚合.最后,通过多层感知机以及解码器-编码器的方式将局部特征进行结合,实现3D点云的分类与分割.该方法考虑了输入嵌入时单个点在点云中的局部语境信息,构建局部长距离下的网络结构,最终得到的结果更具区分度.在ShapeNetPart、RoofN3D等数据集上的实验证实所提方法的分类与分割性能较优.     

三维模型数字水印技术研究进展

数字水印技术为网络环境下的三维模型提供版权保护。介绍三维模型数字 水印的特征、分类及其攻击技术,重点分析三维点云模型、三维网格模型、参数曲面模型、 体数据模型数字水印技术以及三维模型数字水印算法性能评价的研究现状,归纳、总结出三 维模型数字水印技术的研究难点以及需要深入研究的问题。     

点云算法在医学领域的研究进展

点云作为一种重要的3维数据,能够直观地模拟生物器官、组织等的3维结构,基于医学点云数据的分类、分割、配准、目标检测等任务可以辅助医生进行更为准确的诊断和治疗,在临床医学以及个性化医疗器械辅助设计与3D打印有着重要的应用价值。随着深度学习的发展,越来越多的点云算法逐步由传统算法扩展到深度学习算法中。本文对点云算法在医学领域的研究及其应用进行综述,旨在总结目前用于医学领域的点云方法,包括医学点云的特点、获取途径以及数据转换方法;医学点云分割中的传统算法和深度学习算法;以及医学点云的配准任务定义、意义,以及基于有/无特征的配准方法。总结了医学点云在临床应用中仍存在的限制和挑战：1）医学图像重建的人体器官点云分布稀疏且包含噪音、误差;2）医学点云数据集标注困难、制作成本高,可用于训练深度学习模型的公开数据集非常稀少;3）前沿的点云处理算法大都基于自然场景点云数据集训练,这些算法在医学点云处理中的鲁棒性和泛化能力还有待验证。随着医学点云数据集质量和数量的提升,医学点云处理算法的研究将会吸引更多的研究者。     

Textured mesh surface reconstruction of large buildings with multi-view stereo

There are three main approaches for reconstructing 3D models of buildings. Laser scanning is accurate but expensive and limited by the laser’s range. Structure-from-motion (SfM) and multi-view stereo (MVS) recover 3D point clouds from multiple views of a building. MVS methods, especially patch-based MVS, can achieve higher density than do SfM methods. Sophisticated algorithms need to be applied to the point clouds to construct mesh surfaces. The recovered point clouds can be sparse in areas that lack features for accurate reconstruction, making recovery of complete surfaces difficult. Moreover, segmentation of the building’s surfaces from surrounding surfaces almost always requires some form of manual inputs, diminishing the ease of practical application of automatic 3D reconstruction algorithms. This paper presents an alternative approach for reconstructing textured mesh surfaces from point cloud recovered by patch-based MVS method. To a good first approximation, a building’s surfaces can be modeled by planes or curve surfaces which are fitted to the point cloud. 3D points are resampled on the fitted surfaces in an orderly pattern, whose colors are obtained from the input images. This approach is simple, inexpensive, and effective for reconstructing textured mesh surfaces of large buildings. Test results show that the reconstructed 3D models are sufficiently accurate and realistic for 3D visualization in various applications.     

基于几何配准的三维模型几何比对方法研究

针对设计师在进行协同建模时无法快速查看模型变更信息的问题,提出了快速比 对两个模型几何差异的方法。首先,通过等步长采点获取两个模型的点云数据;然后,利用主 元分析法计算两个点云的特征向量从而获取点云的参考坐标系,推导两个点云参考坐标系的坐 标变换,将两个点云调整到一致,即可实现点云的初始配准;最后,利用最近点迭代法对两个 点云进行精确配准,记录配准后两个点云中的非重叠区域,可获取两个点云的几何差异区域。 测试结果表明该方法可以有效识别两个模型的几何差异。     

自适应K-means聚类的散乱点云精简

目的 点云精简是曲面重建等点云处理的一个重要前提,针对以往散乱点云精简算法的精简结果存在失真较大、空洞及不适用于片状点云的问题,提出一种自适应K-means聚类的点云精简算法。方法 首先,根据k邻域计算每个数据点的曲率、点法向与邻域点法向夹角的平均值、点到邻域重心的距离、点到邻域点的平均距离,据此运用多判别参数混合的特征提取方法识别并保留特征点,包括曲面尖锐点和边界点;然后,对点云数据建立自适应八叉树,为K-means聚类提供与点云密度分布相关的初始化聚类中心以及K值;最后,遍历整个聚类,如果聚类结果中含有特征点则剔除其中的特征点并更新聚类中心,计算更新后聚类中数据点的最大曲率差,将最大曲率差大于设定阈值的聚类进行细分,保留最终聚类中距聚类中心最近的数据点。结果 在聚类方面,将传统的K-means聚类和自适应K-means聚类算法应用于bunny点云,后者在聚类的迭代次数、评价函数值和时间上均优于前者;在精简方面,将提出的精简算法应用于封闭及片状两种不同类型的点云,在精简比例为1/5时fandisk及saddle模型的精简误差分别为0.29×10^-3、-0.41×10^-3和0.037、-0.094,对于片状的saddle点云模型,其边界收缩误差为0.030 805,均小于栅格法和曲率法。结论 本文提出的散乱点云精简算法可应用于封闭及片状点云,精简后的数据点分布均匀无空洞,对片状点云进行精简时能够保护模型的边界数据点。     

保特征的点云骨架提取算法

三维模型的骨架提取是计算机图形学中一个重要的研究方向。对于有噪声的点云模型,曲线骨 架提取的难点在于保持正确的拓扑结构以及良好的中心性;对于无噪声的点云模型,曲线骨架提取的难点在于 对模型细节特征的保留。目前主流的点云骨架提取方法往往无法同时解决这 2 个难点。算法在最优传输理论的 基础之上结合聚类的思想,将点云骨架提取的问题转化为一个最优化问题。首先使用最优传输得到原始点云与 采样点云之间的传输计划。然后使用聚类的思想将原始点云进行分割,采样点即成为了簇的中心。接着通过簇 与簇之间的调整与合并减少聚类个数,优化聚类结果。最后通过迭代的方式得到粗糙的骨架并使用插点操作进 行优化。大量实验结果表明,该算法在有噪声与无噪声的三维点云模型上均能提取出质量良好的曲线骨架并保 留模型的特征。     

多特征融合与几何卷积的机载LiDAR点云地物分类

目的 点云分类传统方法中大量依赖人工设计特征,缺乏深层次特征,难以进一步提高精度,基于深度学习的方法大部分利用结构化网络,转化为其他表征造成了3维空间结构信息的丢失,部分利用局部结构学习多层次特征的方法也因为忽略了机载数据的几何信息,难以实现精细分类。针对上述问题,本文提出了一种基于多特征融合几何卷积神经网络（multi-feature fusion and geometric convolutional neural network,MFFGCNN）的机载LiDAR （light detection and ranging）点云地物分类方法。方法 提取并融合有效的浅层传统特征,并结合坐标尺度等预处理方法,称为APD模块（airporne laser scanning point cloud design module）,在输入特征层面对典型地物有针对性地进行信息补充,来提高网络对大区域、低密度的机载LiDAR点云原始数据的适应能力和基础分类精度,基于多特征融合的几何卷积模块,称为FGC （multi-feature fusion and geometric convolution）算子,编码点的全局和局部空间几何结构,实现对大区域点云层次化几何结构的获取,最终与多尺度全局的逐点深度特征聚合提取高级语义特征,并基于空间上采样获得逐点的多尺度深度特征实现机载LiDAR点云的语义分割。结果 在ISPRS （International Society for Photogrammetry and Remote Sensing）提供的3维标记基准数据集上进行模型训练与测试,由于面向建筑物、地面和植被3类典型地物,对ISPRS的9类数据集进行了类别划分。本文算法在全局准确率上取得了81.42%的较高精度,消融实验结果证明FGC模块可以提高8%的全局准确率,能够有效地提取局部几何特性,相较仅基于点的3维空间坐标方法,本文方法可提高15%的整体分类精度。结论 提出的MFFCGNN网络综合了传统特征的优势和深度学习模型的优点,能够实现机载LiDAR点云的城市重要地物快速分类。     

一种基于双线激光的三维测量方法

三维扫描系统在扫描过程中经常会遇到被测物体遮挡了CCD的可视区域导致扫描数据的丢失问题,从而使重构模型产生缺陷。针对该问题,本文提出一种改进措施通过在以CCD为中心的对称位置增加一路激光器,使得系统可以获取两幅不同角度的点云图,然后将这两幅点云图进行拼接互补了单幅点云图的缺陷。最后,分别对被测物体的上、中、下3个部位的距离及其对应的三维模型的距离进行多组数据的测量,并计算出其均方误差为0.22～0.403mm。     

融合逆密度函数与关系形状卷积神经网络的点云分析

目的 3D点云与以规则的密集网格表示的图像不同,不仅不规则且无序,而且由于输入输出大小和顺序差异,具有密度不均匀以及形状和缩放比例存在差异的特性。为此,提出一种对3D点云进行卷积的方法,将关系形状卷积神经网络（relation-shape convolution neural network,RSCNN）与逆密度函数相结合,并在卷积网络中增添反卷积层,实现了点云更精确的分类分割效果。方法 在关系形状卷积神经网络中,将卷积核视为由权重函数和逆密度函数组成的3D点局部坐标的非线性函数。对给定的点,权重函数通过多层感知器网络学习,逆密度函数通过核密度估计（kernel density estimation,KDE）学习,逆密度函数的引入对点云采样率不均匀的情况进行弥补。在点云分割任务中,引入由插值和关系形状卷积层两部分组成的反卷积层,将特征从子采样点云传播回原始分辨率。结果 在ModelNet40、ShapeNet、ScanNet数据集上进行分类、部分分割和语义场景分割实验,验证模型的分类分割性能。在分类实验中,与PointNet++相比,整体精度提升3.1%,在PointNet++将法线也作为输入的情况下,精度依然提升了1.9%;在部分分割实验中,类平均交并比（mean intersection over union,mIoU）比PointNet++在法线作为输入情况下高6.0%,实例mIoU比PointNet++高1.4%;在语义场景分割实验中,mIoU比PointNet++高13.7%。在ScanNet数据集上进行不同步长有无逆密度函数的对比实验,实验证明逆密度函数将分割精度提升0.8%左右,有效提升了模型性能。结论 融合逆密度函数的关系形状卷积神经网络可以有效获取点云数据中的局部和全局特征,并对点云采样不均匀的情况实现一定程度的补偿,实现更优的分类和分割效果。     

Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition

Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment.     

基于深度学习的点云语义分割研究综述

近年来,深度传感器和三维激光扫描仪的普及推动了三维点云处理方法的快速发展。点云语义分割作为理解三维场景的关键步骤,受到了研究者的广泛关注。随着深度学习的迅速发展并广泛应用到三维语义分割领域,点云语义分割效果得到了显著提升。主要对基于深度学习的点云语义分割方法和研究现状进行了详细的综述。将基于深度学习的点云语义分割方法分为间接语义分割方法和直接语义分割方法,根据各方法的研究内容进一步细分,对每类方法中代表性算法进行分析介绍,总结每类方法的基本思想和优缺点,并系统地阐述了深度学习对语义分割领域的贡献。然后,归纳了当前主流的公共数据集和遥感数据集,并在此基础上对比主流点云语义分割方法的实验结果。最后,对语义分割技术未来的发展方向进行了展望。