Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

A modified method of discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces

This paper presents an automated method for discontinuity trace mapping using three-dimensional point clouds of rock mass surfaces.Specifically,the method consists of five steps:(1)detection of trace feature points by normal tensor voting theory,(2)co ntraction of trace feature points,(3)connection of trace feature points,(4)linearization of trace segments,and(5)connection of trace segments.A sensitivity analysis was then conducted to identify the optimal parameters of the proposed method.Three field cases,a natural rock mass outcrop and two excavated rock tunnel surfaces,were analyzed using the proposed method to evaluate its validity and efficiency.The results show that the proposed method is more efficient and accurate than the traditional trace mapping method,and the efficiency enhancement is more robust as the number of feature points increases.

GeeNet:robust and fast point cloud completion for ground elevation estimation towards autonomous vehicles

Ground elevation estimation is vital for numerous applications in autonomous vehicles and intelligent robotics including three-dimensional object detection,navigable space detection,point cloud matching for localization,and registration for mapping.However,most works regard the ground as a plane without height information,which causes inaccurate manipulation in these applications.In this work,we propose GeeNet,a novel end-to-end,lightweight method that completes the ground in nearly real time and simultaneously estimates the ground elevation in a grid-based representation.GeeNet leverages the mixing of two-and three-dimensional convolutions to preserve a lightweight architecture to regress ground elevation information for each cell of the grid.For the first time,GeeNet has fulfilled ground elevation estimation from semantic scene completion.We use the SemanticKITTI and SemanticPOSS datasets to validate the proposed GeeNet,demonstrating the qualitative and quantitative performances of GeeNet on ground elevation estimation and semantic scene completion of the point cloud.Moreover,the crossdataset generalization capability of GeeNet is experimentally proven.GeeNet achieves state-of-the-art performance in terms of point cloud completion and ground elevation estimation,with a runtime of 0.88 ms.

An unmanned ground vehicle phenotyping-based method to generate three-dimensional multispectral point clouds for deciphering spatial heterogeneity in plant traits

Fusing three-dimensional(3D)and multispectral(MS)imaging data holds promise for high-throughput and comprehensive plant phenotyping to decipher genome-to-phenome knowledge.Acquiring high-quality 3D MS point clouds(3DMPCs)of plants remains challenging because of poor 3D data quality and limited radiometric calibration methods for plants with a complex canopy structure.Here,we present a novel 3D spatial–spectral data fusion approach to collect high-quality 3DMPCs of plants by integrating the next-best-view planning for adaptive data acquisition and neural reference field(NeREF)for radiometric calibration.This approach was used to acquire 3DMPCs of perilla,tomato,and rapeseed plants with diverse plant architecture and leaf morphological features evaluated by the accuracy of chlorophyll content and equivalent water thickness(EWT)estimation.The results showed that the completeness of plant point clouds collected by this approach was improved by an average of 23.6%compared with the fixed viewpoints alone.The NeREF-based radiometric calibration with the hemispherical reference outperformed the conventional calibration method by reducing the root mean square error(RMSE)of 58.93%for extracted reflectance spectra.The RMSE for chlorophyll content and EWT predictions decreased by 21.25%and 14.13%using partial least squares regression with the generated 3DMPCs.Collectively,our study provides an effective and efficient way to collect high-quality 3DMPCs of plants under natural light conditions,which improves the accuracy and comprehensiveness of phenotyping plant morphological and physiological traits,and thus will facilitate plant biology and genetic studies as well as crop breeding.

Integration system research and development for three-dimensional laser scanning information visualization in goaf

An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.

A state-of-the-art review of automated extraction of rock mass discontinuity characteristics using three-dimensional surface models

In the last two decades,significant research has been conducted in the field of automated extraction of rock mass discontinuity characteristics from three-dimensional(3D)models.This provides several methodologies for acquiring discontinuity measurements from 3D models,such as point clouds generated using laser scanning or photogrammetry.However,even with numerous automated and semiautomated methods presented in the literature,there is not one single method that can automatically characterize discontinuities accurately in a minimum of time.In this paper,we critically review all the existing methods proposed in the literature for the extraction of discontinuity characteristics such as joint sets and orientations,persistence,joint spacing,roughness and block size using point clouds,digital elevation maps,or meshes.As a result of this review,we identify the strengths and drawbacks of each method used for extracting those characteristics.We found that the approaches based on voxels and region growing are superior in extracting joint planes from 3D point clouds.Normal tensor voting with trace growth algorithm is a robust method for measuring joint trace length from 3D meshes.Spacing is estimated by calculating the perpendicular distance between joint planes.Several independent roughness indices are presented to quantify roughness from 3D surface models,but there is a need to incorporate these indices into automated methodologies.There is a lack of efficient algorithms for direct computation of block size from 3D rock mass surface models.

Point cloud completion via structured feature maps using a feedback network

In this paper,we tackle the challenging problem of point cloud completion from the perspective of feature learning.Our key observation is that to recover the underlying structures as well as surface details,given partial input,a fundamental component is a good feature representation that can capture both global structure and local geometric details.We accordingly first propose FSNet,a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions.We then integrate FSNet into a coarse-to-fine pipeline for point cloud completion.Specifically,a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud.Next,a point cloud upsampling network is used to generate a dense point cloud from the partial input and the coarse intermediate output.To efficiently exploit local structures and enhance point distribution uniformity,we propose IFNet,a point upsampling module with a self-correction mechanism that can progressively refine details of the generated dense point cloud.We have conducted qualitative and quantitative experiments on ShapeNet,MVP,and KITTI datasets,which demonstrate that our method outperforms stateof-the-art point cloud completion approaches.

基于残差优化的综采工作面煤壁点云补全方法

煤矿综采工作面巷道的数字化三维重建过程中需要完整且密集的煤壁点云数据。受遮挡、视角限制等因素影响,采集的综采工作面煤壁点云数据往往不完整且稀疏,影响下游任务,需进行煤壁点云修复和补全。目前缺少针对井下点云补全任务的数据集和网络模型,现有模型用于煤壁点云补全时存在点云密度分布不均匀、点云特征信息丢失等情况。针对上述问题,设计了一种基于残差优化的煤壁点云补全网络模型,采用监督学习方式学习点云特征信息,通过最小化密度采样和残差网络迭代优化输出完整点云。采集煤矿井下真实综采工作面煤壁点云数据,预处理后筛选可用数据,通过模拟随机空洞制作煤壁点云缺失数据集,并用缺失数据集训练基于残差优化的煤壁点云补全网络模型。实验结果表明:与经典的FoldingNet,TopNet,AtlasNet,PCN,3D-Capsule点云补全网络模型相比,基于残差优化的煤壁点云补全网络模型针对构造的缺失煤壁点云和稀疏煤壁点云补全的倒角距离、地移距离及F1分数均能达到最优水平,整体补全效果最佳;针对实际缺失的煤壁点云,该模型能够实现有效补全。

双分支结构的多层级三维点云补全

为了缓解现有点云补全方法在特征提取过程中很难平衡局部特征和全局特征的问题,提出了一种双分支结构的多层级点云补全算法。利用两个独立的分支网络分别提取出输入点云的局部特征信息和全局特征信息,再将两种特征信息进行拼接形成特征向量。使用五层联合感知机将特征向量映射成多个维度,进而提取多维特征信息并将其整合成最终特征向量。采用金字塔结构在256、512、1024特征维度上对最终特征向量进行特征解码,预测三种不同分辨率的点云。引入鉴别器网络,通过联合训练鉴别器产生的对抗损失和分层重建点云产生的补全损失去优化网络。在ShapeNet数据集上进行实验,算法显著提升了点云补全精度,并且在缺失大面积点云时也能恢复出较为完善的物体形状。

融合形状结构恢复和细节补偿的双分支点云修复网络

针对传统点云修复中难以有效地保持原始形状细节结构信息的问题,提出一种融合形状全局结构恢复和局部细节补偿的双分支点云形状修复网络.网络中的形状全局结构恢复分支为编解码-解码器结构,编码器对缺失点云数据进行特征变换以克服点云形状的旋转不变性,利用最大池化操作解决点云的无序性问题,并通过多层感知器生成原始点云的特征码字,解码器对编码得到的特征码字使用4个二维网格进行2次折叠操作,拟合点云形状得到粗修复结果;为了补偿点云粗修复结果的形状细节信息,网络中的局部细节补偿分支对编码器提取得到的不同维度特征,通过层次特征学习和多层次特征融合学习点云形状的几何结构特征,有效地恢复缺失点云数据并保留原始形状细节信息;最终将经全局结构恢复分支和局部细节补偿分支分别得到的点云数据拼接融合,再进行迭代最远点重采样,得到点云形状精修复结果.实验结果表明,在ShapeNet数据集上,所提网络比已有网络修复结果的平均CD误差和平均EMD误差分别低16%~29%和19%~65%;在ModelNet数据集上,比已有网络修复结果的平均CD误差和平均EMD误差分别低6%~41%和31%~59%;该网络可以修复原始形状的整体结构信息并能有效地恢复其形状细节,生成采样点分布均匀的完整点云模型,且对模型噪声和不同程度的模型缺失均具有鲁棒性.

基于点代理增强和逐层上采样的猪体点云补全方法

采用逆向工程技术进行猪体的三维重建并测算,是低成本无接触式猪体型体况评估的一大解决方案,在比较单视角和多视角采集方法的优缺点后,本文提出基于深度学习的点云补全方法,将猪体局部点云恢复成一个完整的点云以实现猪体三维重建。该猪体点云补全方法基于点代理增强和逐层上采样,首先通过特征提取结合位置嵌入生成点代理,使用点代理增强Transformer进一步提高点代理的特征表示能力,再基于点代理通过逐层上采样由粗到细逐步恢复最终的高分辨率、细粒度和分布均匀的完整点云。本文对实际生产环境中采集的猪体点云进行补全,所提方法与目前主流的点云补全方法进行对比试验,在多个指标的评定上,本文提出的方法都取得了较好性能,尤其是在猪体点云缺失严重补全难度较大的情况下效果更为突出。试验证明该方法对猪体主干部位的补全具备应用价值,能够用于实现基于局部点云的猪体三维点云重建。

基于车载三维激光扫描的城市道路竣工测量探讨

车载三维激光扫描系统融合了多种传感器和数据源,可以自动、迅速地获取道路的全方位信息。其扫描速度迅捷、数据信息丰富、精确度高、采集过程安全简单,并能节省人力。此技术显著提高了外业生产效率,并降低了生产成本。对车载三维激光扫描技术在道路工程竣工测量中的内外业处理流程的研究结果表明:该技术的精度可达到1∶500测图精度要求,满足城市高架路竣工规划测绘的精度需求。该技术方案是切实可行的,且能高效地提高生产效率。

基于CGRSNet的残缺油桃外形点云补全方法

[目的]利用深度相机采集到的油桃数据在进行三维重建时,存在摄像头视野限制,导致油桃点云出现缺失的现象,严重影响油桃表型分析的准确度。为了提高油桃分级分类的准确率和工作效率,提出一种基于三维重建的油桃片外形参数估测方法。[方法]提出了一种基于真实结构的粗粒度点云生成网络(coarse-grained realistic structure with point cloud generation network,CGRSNet)。该网络首先通过编码、解码机制构建点云补全网络,通过新的采样算法对原始点云提取不同维度下的特征信息,提升油桃表型的特征提取能力,然后自解码器自上而下进行多维度点云补全,最终得到完整的油桃形状,最后用模型的参数预估模块预估油桃体积。利用多组参数构建的油桃几何模型离散成点云数据输入CGRSNet网络,得到油桃几何模型外形补全的预训练模型,再利用真实油桃点云数据对预训练模型CGRSNet做模型迁移的参数微调。[结果]该补全网络对油桃数据集补全结果的倒角距离为0.196 cm。经线性回归分析,得出体积估测的RMSE和R^(2)分数为2.47 cm^(3)和0.94,相较于PF-Net的RMSE和R^(2)分数提升约0.88 cm^(3)和0.01。[结论]本文提出的基于CGRSNet的油桃外形参数估测算法具有较好的实用性和精确度。

SLAM激光扫描技术在地铁隧道竣工测量中的应用

轴线偏差和断面测量一直是隧道竣工测量的两个重要指标。传统使用全站仪或断面仪进行测量的方式不仅作业效率低,采集的数据量也较少,难以反映隧道内的真实情况,无法满足隧道竣工测量的要求。本文提出了一种基于SLAM手持激光扫描仪进行地铁隧道竣工测量的方法,并在上海市某在建地铁隧道内进行了现场试验。比较激光点云后处理得到的断面中心与设计数据,二者平均点位精度小于3 cm。这表明使用SLAM手持激光扫描仪进行地铁隧道竣工测量的方法有效可行,精度能够满足竣工测量的要求,具有广阔的应用前景。

融合图像信息的跨模态Transformer点云补全算法

针对三维传感器(如LiDAR、深度相机)获取的点云往往残缺不全,需要进行补全处理,而单模态方法存在的补全结果细节不丰富、结构不完整等问题,提出一种融合图像信息的跨模态Transformer点云补全算法.首先采用点云分支和图像分支分别提取点云特征和图像特征,其中,点云分支采用PoinTr为骨干网络,图像分支采用7层卷积;然后通过特征融合模块融合点云特征和图像特征,由粗到精地生成全分辨率的点云.在ShapeNet-ViPC数据集上进行实验的结果表明,所提算法的可视化结果优于单模态点云补全方法和目前仅有的跨模态点云补全方法ViPC,且在大部分测试类别上的CD-L_(2)量化指标优于ViPC;平均CD-L_(2)为2.74,比ViPC低17%.为了便于研究人员评估和使用,文中算法可通过https://github.com/Starak-x/ImPoinTr开源获取.

基于点特征传递的电力设备部件激光点云补全网络

针对电力场景下因激光雷达扫描范围有限、电力设备部件相互遮挡等因素导致电力设备部件点云缺失的问题,提出了基于点特征传递的电力设备部件激光点云补全网络PPC-Net(PowerPointcloudCompleteNet)。该网络使用多尺度特征融合编码器提取不同尺度输入残缺点云的全局和局部特征,以避免多维度映射特征导致的电力设备部件细节特征丢失问题,并引入EdgeConv加强对点云邻域信息提取;在精细完整点云生成阶段提出DT模块整合父级点到子级点的特征传递,以保留生成点云的局部特征;设计平滑优化模块,经三级平滑采样算法输出分布均匀、表面平滑的电力设备部件完整点云。在自建电力设备部件点云数据集ELE及公开数据集PCN上实验表明,PPC-Net对残缺的电力设备部件点云有较好的补全效果,并在一般形状点云上有良好泛化性。

基于多尺度特征融合的由粗到精点云形状补全

为了以由粗到精的方式实现点云形状补全,提出一个端到端的两阶段多尺度特征融合网络,其中的每个阶段都是由一个编码器-解码器构成.第1阶段中,首先利用点集抽取模块提取残缺点云的全局特征,在获取不同分辨率点特征的同时能关注更多的局部邻域特征,然后使用多层感知机作为解码器生成粗糙的点云骨架;第2阶段中,利用点云骨架和残缺点云提取多尺度局部特征,并通过注意力机制与第1阶段中的多尺度全局特征相互融合,使得每个点都包含全局和局部几何信息;最后将第2阶段中的全局特征和多尺度局部特征逐步进行上采样,并通过多层感知机生成精细的完整点云.采用倒角距离作为评价标准,在ShapeNet,MVP和Completion3D数据集上进行点云补全实验的结果表明,误差分别比基准网络降低17.1%,3.9%和13.9%,验证了所提网络的有效性.

高反光物体的局部点云孔洞补全方法

针对高反光物体点云数据存在噪声明显、孔洞过大,使用传统贪婪投影三角化算法无法达到点云补全目的的问题,提出一种改进的贪婪三角化点云补全算法。利用统计滤波和高斯滤波进行离群点去除和平滑处理;采用移动最小二乘法对局部点云进行上采样增强数据,并且对点云进行进一步平滑。将贪婪三角化算法中的kd树(k-dimensional tree)算法替换为效率更高的八叉树(octree)搜索算法,将主成分分析法替换为准确率更高的移动最小二乘法进行法线估计;最后,进行点云三角化,完成点云补全。实验结果表明,改进算法能够更好地补全孔洞,补全后表面更加平滑、结构更为准确,且花费时间更短。

移动手持激光扫描仪在建筑物竣工测量中的应用

随着科技的快速发展,测绘领域正迎来一场技术革新,三维激光扫描等新型测绘技术引领测绘地理信息服务向更为智能的方向发展。为研究测绘新技术在城市工程测量中的应用潜能,本文将移动手持激光扫描仪引入建筑物竣工测量中。基于ZEB-REVO RT手持激光扫描仪获取竣工项目区域的点云数据,经点云解算和坐标转换,得到满足竣工测量条件的激光点云数据。基于点云数据,完成建筑物的地形提取与编辑,并借助传统的高精度测绘手段,对竣工测量成果进行精度检验。试验结果表明,基于手持激光点云数据的建筑物竣工成果,点位平面精度为±4.6 cm,边长精度为±2.4 cm,符合《城市测量规范CJJ/T 8—2011》中建筑物竣工测量精度标准。本文研究成果验证了手持激光扫描仪在建筑物竣工测量中的有效性与实用性,为测绘新技术在工程测量领域的应用提供了实践经验与参考。

一种改进的小组立工件图像处理算法

针对三维相机拍摄得到的小组立复杂结构件点云数据,建立小组立焊接机器人的整体视觉算法系统。在处理点云图像时,发现阴影和常规点云滤波方法可能会破坏模型,因此采用离群滤波算法对点云数据进行清理,估计提取的特征向量,并结合曲率滤波算法去除小范围阴影噪声平面。经过设备调试和生产验证,成功剔除噪声点,并有效保留三维模型的尖锐特征,为后续点云配准和三维重建提供基础。整个小组立三维相机处理系统可涵盖大部分小组立复杂结构件,这一研究成果可为船厂小组立焊缝识别视觉系统的设计提供有益参考。