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.

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.

Hand Gesture Recognition Using Appearance Features Based on 3D Point Cloud

This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.

基于FPFH-PointNet的术中膝关节软骨表面点云自动提取

目的机器人辅助膝关节置换术导航系统采用激光扫描仪获取术中软骨点云,并与术前模型配准,实现自动非接触空间注册。术中患者膝关节病变点云包含大量的肌肉、肌腱、韧带及手术器械等无关背景点云。手动去除无关点云会因人机交互而占据手术时间,因此,本研究提出一种新颖的膝关节软骨表面点云自动提取方法,以便快速精准实施术中注册。方法PointNet因缺乏软骨表面和几何局部信息的充分描述,不能高精度地提取软骨点云。本研究提出了一种结合快速点特征直方图FPFH-PointNet方法,该方法有效地描述了软骨点云表观,实现了软骨表面点云的自动高效分割。结果本研究以从不同角度扫描10例尸体膝关节标本和1例人腿模型的股骨远端软骨点云为数据集。PointNet和FPFH-PointNet分割软骨点云的准确率分别为0.94±0.003和0.98±0,平均交并比(mIOU)分别为0.83±0.015和0.93±0.005。FPFH-PointNet相比于PointNet,准确率和mIOU分别提高了4%和10%,而耗时仅约为1.37 s。结论FPFH-PointNet能够精准地自动提取术中膝关节软骨点云,满足了术中导航的性能需求。

基于毫米波雷达稀疏点云的人体行为识别方法

目前利用毫米波雷达进行人体行为识别的方法在复杂场景下无法很好地区分相似动作,与此同时模型的鲁棒性和抗干扰能力也相对较差;针对以上两个问题,提出了一种通用的基于毫米波雷达稀疏点云的人体行为识别方法,该方法首先利用K-means++聚类算法对点云进行采样,然后使用基于注意力特征融合的点云活动分类网络进行人体行为特征的提取和识别,该网络可以兼顾点云的空间特征以及时序特征,对稀疏点云的运动有灵敏的感知能力;为了验证所提出方法的有效性和鲁棒性,分别在MMActivity数据集和MMGesture数据集上进行了实验,并在两个数据集上取得97.50%和94.10%的准确率,均优于其它方法;此外,进一步验证了K-means++点云采样方法的有效性,相较于随机采样,准确率提升了0.4个百分点,实验结果表明所提出方法能够有效地提升人体行为识别的准确率,且模型具有较好的泛化能力。

基于真实三维人体点云的特征参数提取

针对真实三维人体点云数据,提出一种快速提取人体特征参数的方法。首先对点云数据进行预处理,并将人体中心点转化为坐标原点,然后利用人体几何形状以及特征参数的定义,通过分层探索法来定位人体特征点,最后利用人体特征点得到相应的特征截面信息,使用边缘识别法提取特征轮廓边界点,对于不同人体体型,基于人体几何形状分析法自动识别正常和肥胖人体体型,有效地计算不同体型的真实特征参数信息。数值实验说明了该方法的有效性。

基于云计算的优化配网调度执行系统研究

为了优化配网调度执行工作,对配网调度进行了研究。设计了基于云计算的优化配网调度执行系统。在硬件设计中采用分布式结构执行配网调度。在系统软件部分,使用云计算技术,设计了主动配网云边协同计算体系。该体系实现了配电调度边缘计算节点的远程操作,提高了配网调度执行优化的能力。采用客户端/服务端(C/S)、浏览器/服务器(B/S)混合架构,实现了配网调度的人机可视化执行。利用两点估计法以提高配网调度模型的优化计算能力,进而实现配网调度的控制,提升了配网调度运行的稳定性与经济性。试验结果表明,在对配网调度执行的可靠性进行测试时,该系统的可靠性达到了96%。该系统可使配网始终处于安全、可靠、优质、经济、高效的运行状态,为配网调度执行提供了一种可行的方案。

基于角度半径的青年男子躯干部形态相似性匹配

为实现基于三维人体点云的人体形态相似性匹配,文章提出了利用角度半径进行青年男体躯干部轮廓形态描述的方法。先以187名18~25周岁的青年男大学生为研究对象,使用三维扫描法获取其三维点云数据。再通过逆向工程软件识别人体躯干部特征点,截取其正侧面躯干轮廓形态。然后分别建立正侧面躯干部坐标系,提取27个正面角度半径值和60个侧面角度半径值形成三维点云躯干形态数据库。选取14名被试者进行验证,提取的正、侧面角度半径为样本,采用均方根误差法与点云截面数据库进行匹配,并使用OpenCV框架中的MatchSharp法验证匹配结果。结果显示,13名被试者的躯干部正侧面轮廓形态与三维点云轮廓形态匹配成功,准确率达92.86%。研究结果可为三维点云形态相似性分析提供一定的理论基础。

Three-dimensional face point cloud hole-filling algorithm based on binocular stereo matching and a B-spline

When obtaining three-dimensional(3D)face point cloud data based on structured light,factors related to the environment,occlusion,and illumination intensity lead to holes in the collected data,which affect subsequent recognition.In this study,we propose a hole-filling method based on stereo-matching technology combined with a B-spline.The algorithm uses phase information acquired during raster projection to locate holes in the point cloud,simultaneously extracting boundary point cloud sets.By registering the face point cloud data using the stereo-matching algorithm and the data collected using the raster projection method,some supplementary information points can be obtained at the holes.The shape of the B-spline curve can then be roughly described by a few key points,and the control points are put into the hole area as key points for iterative calculation of surface reconstruction.Simulations using smooth ceramic cups and human face models showed that our model can accurately reproduce details and accurately restore complex shapes on the test surfaces.Simulation results indicated the robustness of the method,which is able to fill holes on complex areas such as the inner side of the nose without a prior model.This approach also effectively supplements the hole information,and the patched point cloud is closer to the original data.This method could be used across a wide range of applications requiring accurate facial recognition.

一种三维人体模型快速测量方法

三维人体的逆向建模在服装领域具有重要应用价值,然而测量人体模型时难以保证长时间的固定姿态,且人体表面不能喷涂纹理特征,急需实现非接触的人体模型高精度快速测量。该文提出一种数字散斑投射的高精度快速扫描方法,通过十参数畸变模型,实现相机的高精度标定;利用双目立体视觉原理,构建三维人体快速扫描的传感测量装置;通过大视场标定与坐标统一技术,结合彩色摄像机和散斑投影仪实现包含面部的人体全方位多视角图像;利用立体视觉重建技术重建得到完整的三维人体点云模型,并进行点云后处理。通过实验验证该文方法标定15次得到的重投影误差,测量结果均优于0.05 pixels,说明所提出的三维人体快速测量方法测量精度高。3组人体扫描实验结果充分表明:该文提出的方法可以在0.1 s内完成人体扫描,并且校准误差均小于0.1 pixels,可以实现对人体的快速、高精度扫描。

三维点云表示的人体动作序列预测

目前对三维人体动作序列的预测工作相对较少,且主要使用三角形网格表示人体模型,不如三维点云那样简单又容易获取。为此,该文用三维点云表示人体模型,提出一种基于MeteorNet的点云动作序列预测方法。将动作序列中不同时刻的三维点云融合在一起,寻找点的时空邻域进行分组;叠加三层Meteor模块在时空邻域聚合信息,以获取点云序列的时空特征;通过三层全连接网络预测动作的点云坐标。实验结果表明,该方法预测出的人体动作与真实动作的误差较小。

基于时空注意的毫米波雷达人体活动识别网络

为了解决传统方式利用摄像头进行人体活动识别抗干扰性差以及侵犯用户隐私的问题,提出一种基于时空注意的毫米波雷达3D点云数据的人体活动识别网络,以实现智能应用上下文的准确感知。该网络首先使用二级滑动时间窗口分别累积和分离人体活动产生的点云数据作为分类器的输入,利用PointLSTM单元根据点云坐标关系聚合点特征和状态以提取人体活动的时间序列特征;然后拼接时空特征,通过采样分组模块降低整体网络计算量以及提升网络对局部特征的聚合能力;最后使用堆叠的注意力模块深度融合动态点云数据时空上的全局和局部特征以完成对人体活动任务的准确分类。利用毫米波雷达采集了多种人体活动点云数据集,实验结果表明,提出的时空注意网络平均准确度可达98.64%,能够有效识别复杂且差异小的人体活动类型,完成人体活动识别系统的要求。

A non-contact measurement method for rock mass discontinuity orientations by smartphone

Smartphones are usually packed with a large number of features.An increasing number of researchers are paying attention to the technological capabilities of smartphones,which is a new topic and research interest.This paper proposes a method using smartphones and digital photogrammetry to measure the discontinuity orientation of a rock mass.Smartphone photos satisfying a certain overlap rate provide an efficient method for generating point cloud models of rock outcrops based on image matching.Using the target and the generated point cloud model allows for determining actual geographic coordinates and the measurement of discontinuity orientations.The method proposed has been applied to two different study areas.The discontinuity orientations measured by the proposed method are compared with those measured by the manual method in two cases.The results show a good agreement,verifying the reliability and accuracy of the proposed method.The main contribution of this paper is to use knowledge of coordinate rotation to determine the actual geographic location of the model through a square target.The equipment used in this study is simple,and photogrammetric field surveys are easy to carry out.

Deep learning-based semantic segmentation of human features in bath scrubbing robots

With the rise in the aging population,an increase in the number of semidisabled elderly individuals has been noted,leading to notable challenges in medical and healthcare,exacerbated by a shortage of nursing staff.This study aims to enhance the human feature recognition capabilities of bath scrubbing robots operating in a water fog environment.The investigation focuses on semantic segmentation of human features using deep learning methodologies.Initially,3D point cloud data of human bodies with varying sizes are gathered through light detection and ranging to establish human models.Subsequently,a hybrid filtering algorithm was employed to address the impact of the water fog environment on the modeling and extraction of human regions.Finally,the network is refined by integrating the spatial feature extraction module and the channel attention module based on PointNet.The results indicate that the algorithm adeptly identifies feature information for 3D human models of diverse body sizes,achieving an overall accuracy of 95.7%.This represents a 4.5%improvement compared with the PointNet network and a 2.5%enhancement over mean intersection over union.In conclusion,this study substantially augments the human feature segmentation capabilities,facilitating effective collaboration with bath scrubbing robots for caregiving tasks,thereby possessing significant engineering application value.

基于激光三维扫描的人体特征尺寸测量

提出了一种利用三维人体点云数据测量人体关键尺寸的方法。应用线结构光激光三维扫描仪对特定站姿的人体进行扫描,获得完整三维人体点云。根据人体视线和脚尖的同向性及两腿的自然分开特征,对点云进行方位调整,使人体朝向z轴正方向。利用手动删除和最大连通域删除相结合删除噪声点。确定人体的6个特征点和20个特征平面,分别应用对应截面y坐标差值法测量长度尺寸,凸壳法测量截面周长,测量了34个人体关键长度和周长,对影响测量精度的因素进行了分析。结果表明,典型尺寸测量误差小于3%,可满足人类工效学等领域快速测量人体特征尺寸的要求。

基于Kinect深度信息的手势提取与识别研究

针对基于视觉的手势识别技术对环境背景要求较高的问题,提出了一种使用深度信息进行手势提取和识别的研究方案。采用了微软Kinect摄像头进行手势深度图的采集,再将深度图转换为三维点云,根据深度信息过滤来提取手势数据。对手势数据进行方向校正后统计手势数据中深度信息的区间分布特征并输入到支持向量机进行训练,从而实现了对数字手势1～5的手势识别。实验结果证明,该手势识别方案的平均识别率达到95%,使用设备简单且精度较高,鲁棒性较好。

浊点萃取-高效液相色谱法同时测定人尿中的4种抗凝血鼠药

建立了基于浊点萃取同时测定人尿中杀鼠灵、杀鼠醚、溴敌隆和溴鼠灵的反相高效液相色谱法。以非离子表面活性剂TritonX-114为萃取剂,考察了色谱测定条件,优化了浊点萃取参数。5mL尿样中加入0.030gNaCl,30μL甲酸和5%(w/v)TritonX-114400μL,50℃萃取15min,4种目标物的萃取效率能达到85%以上。在选择的色谱条件下,以V(甲醇)∶V(1%甲酸)=90∶10为流动相,待测组分经反相C18色谱柱分离,于306nm波长处用二极管阵列检测器检测,10min可完成分析。4种鼠药中杀鼠醚在0.01～5.0mg/L,其余3种在0.02～5.0mg/L范围内呈良好的线性关系(r≥0.997);尿样的标准加入回收率为71.2%～114.5%;相对标准偏差小于7.0%。对于5mL尿液,富集相用50μL甲醇稀释,萃取浓缩因子可达到28倍。定量检出浓度分别为:杀鼠灵0.016mg/L,杀鼠醚0.001mg/L,溴敌隆0.005mg/L,溴鼠灵0.003mg/L。本法简便快速、灵敏可靠、绿色环保,能满足鼠药中毒应急检测的要求。