Building 3D CityGML models of mining industrial structures using integrated UAV and TLS point clouds

Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.

Indoor Space Modeling and Parametric Component Construction Based on 3D Laser Point Cloud Data

In order to enhance modeling efficiency and accuracy,we utilized 3D laser point cloud data for indoor space modeling.Point cloud data was obtained with a 3D laser scanner and optimized with Autodesk Recap and Revit software to extract geometric information about the indoor environment.Furthermore,we proposed a method for constructing indoor elements based on parametric components.The research outcomes of this paper will offer new methods and tools for indoor space modeling and design.The approach of indoor space modeling based on 3D laser point cloud data and parametric component construction can enhance modeling efficiency and accuracy,providing architects,interior designers,and decorators with a better working platform and design reference.

3D Object Detection with Attention:Shell-Based Modeling

LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.

Aggregate Point Cloud Geometric Features for Processing

As 3D acquisition technology develops and 3D sensors become increasingly affordable,large quantities of 3D point cloud data are emerging.How to effectively learn and extract the geometric features from these point clouds has become an urgent problem to be solved.The point cloud geometric information is hidden in disordered,unstructured points,making point cloud analysis a very challenging problem.To address this problem,we propose a novel network framework,called Tree Graph Network(TGNet),which can sample,group,and aggregate local geometric features.Specifically,we construct a Tree Graph by explicit rules,which consists of curves extending in all directions in point cloud feature space,and then aggregate the features of the graph through a cross-attention mechanism.In this way,we incorporate more point cloud geometric structure information into the representation of local geometric features,which makes our network perform better.Our model performs well on several basic point clouds processing tasks such as classification,segmentation,and normal estimation,demonstrating the effectiveness and superiority of our network.Furthermore,we provide ablation experiments and visualizations to better understand our network.

Cumulus cloud modeling from images based on VAE-GAN

Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.

An Automated Process of Creating 3D City Model for Monitoring Urban Infrastructures

This paper describes the process of designing models and tools for an automated way of creating 3D city model based on a raw point cloud.Also,making and forming 3D models of buildings.Models and tools for creating tools made in the model builder application within the ArcGIS Pro software.An unclassified point cloud obtained by the LiDAR system was used for the model input data.The point cloud,collected by the airborne laser scanning system(ALS),is classified into several classes:ground,high and low noise,and buildings.Based on the created DEMs,points classified as buildings and formed prints of buildings,realistic 3D city models were created.Created 3D models of cities can be used as a basis for monitoring the infrastructure of settlements and other analyzes that are important for further development and architecture of cities.

3D打印公有云平台运营机制及盈利模式研究

针对当前3D打印技术产业化发展缓慢,难以快速提高中小型企业产业集群快速产品开发能力的问题,分析了当下3D打印技术的应用现状,提出了构建基于面向服务的云制造模式3D打印公有云制造服务平台。从根本上搭建了3D打印公有云制造服务平台的组织架构,提出了"运营主体+技术支撑方+科技能力及其推广服务提供方+科技能力及其推广服务资源渠道"的全流程域、大范围的组织架构,在此基础上研究了平台的运营机制和盈利模式,最后通过实例即云创3d平台商业运营状况,说明了理论研究具有转化成果的可行性。

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise(DBSCAN)algorithm.To identify convective and stratiform clouds in different developmental phases,two-dimensional(2D)and three-dimensional(3D)models are proposed by applying reflectivity factors at 0.5°and at 0.5°,1.5°,and 2.4°elevation angles,respectively.According to the thresholds of the algorithm,which include echo intensity,the echo top height of 35 dBZ(ET),density threshold,andεneighborhood,cloud clusters can be marked into four types:deep-convective cloud(DCC),shallow-convective cloud(SCC),hybrid convective-stratiform cloud(HCS),and stratiform cloud(SFC)types.Each cloud cluster type is further identified as a core area and boundary area,which can provide more abundant cloud structure information.The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing,Xuzhou,and Qingdao.The results show that cloud clusters can be intuitively identified as core and boundary points,which change in area continuously during the process of convective evolution,by the improved DBSCAN algorithm.Therefore,the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification.Because density thresholds are different and multiple elevations are utilized in the 3D model,the identified echo types and areas are dissimilar between the 2D and 3D models.The 3D model identifies larger convective and stratiform clouds than the 2D model.However,the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds.In addition,the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.

The Integrated 3D As-Built Representation of Underground MRT Construction Sites

This study facilitates the scalability of as-built data from an earlier street level to underground transportation sites from the life-cycle perspective of urban information maintenance. As-built 3D scans of a 6 km street were made at different time periods, and of 3 underground Mass Rapid Transit (MRT) stations under construction in Taipei. A scanned point cloud was used to create a Building Information Modeling (BIM) Level of Development (LOD) 500 as-built point cloud model, with which topographic utility data were integrated and the model quality was investigated. The complex underground models of the transportation stations are proofed to be in correct relative locations to the street entrances on ground level. In the future the 3D relationship around the station will facilitate new designs or excavations in the neighborhood urban environment.

3D geological suitability evaluation for underground space based on the AHP-cloud model

Urban development continues to reduce the amount of available ground space.The development of underground space is thus gath-ering increasing attention to alleviate ground congestion.However,there is currently a lack of a three-dimensional(3D)evaluation method to systematically evaluate the geological conditions of underground space and possible geological disaster risks caused by rock and soil masses.This paper presents an engineering geological suitability assessment framework based on 3D geological modeling and an analytic hierarchy process(AHP)-cloud model.As the basis for 3D evaluation,a 3D structural model of the study area is established based on the drilling data and geological profiles.Then the structural model is partitioned to obtain interpolation grids,and the ordinary Kriging interpolation method is applied to attribute interpolation.All the attributes are exported from the geological model,and the rock and soil masses are divided into four categories according to their engineering properties,namely soft soil,sandy soil,cohesive soil,and rock,upon which a targeted hierarchy structure is established based on the attributes that impact the suitability.This paper intro-duces the cloud model to characterize the uncertainty of these evaluation indexes,which synthesizes an AHP method,thus it is referred to as the AHP-cloud model.This new model is used to evaluate the geological suitability of underground space in the Sanlong Bay district,Foshan City,Guangdong,China.In addition,we also determine the excavation difficulty at different depths according to the lithology and weathering degree of the study area.The limitations and future directions of the proposed method are discussed,including the influ-encing factors and weight determination.

机载LiDAR点云和倾斜摄影影像数据融合处理技术流程

机载LiDAR点云和倾斜摄影影像是两种新型测绘地理信息数据,可以用来制作DEM、DOM和建筑三维模型。在分析点云数据和倾斜数据基础上,阐述点云和影像数据融合处理的关键步骤和方法。对点云配准、航带裁切、三维建模以及正射影像制作流程进行了详细阐述,开创了两种数据源融合处理的新模式。

Neighborhood co-occurrence modeling in 3D point cloud segmentation

A significant performance boost has been achieved in point cloud semantic segmentation by utilization of the encoder-decoder architecture and novel convolution operations for point clouds.However,co-occurrence relationships within a local region which can directly influence segmentation results are usually ignored by current works.In this paper,we propose a neighborhood co-occurrence matrix(NCM)to model local co-occurrence relationships in a point cloud.We generate target NCM and prediction NCM from semantic labels and a prediction map respectively.Then,Kullback-Leibler(KL)divergence is used to maximize the similarity between the target and prediction NCMs to learn the co-occurrence relationship.Moreover,for large scenes where the NCMs for a sampled point cloud and the whole scene differ greatly,we introduce a reverse form of KL divergence which can better handle the difference to supervise the prediction NCMs.We integrate our method into an existing backbone and conduct comprehensive experiments on three datasets:Semantic3D for outdoor space segmentation,and S3DIS and ScanNet v2 for indoor scene segmentation.Results indicate that our method can significantly improve upon the backbone and outperform many leading competitors.

3D printing process selection model based on triangular intuitionistic fuzzy numbers in cloud manufacturing

The distributed and customized 3D printing can be realized by 3D printing services in a cloud manufacturing environment.As a growing number of 3D printers are becoming accessible on various 3D printing service platforms,there raises the concern over the validation of virtual product designs and their manufacturing procedures for novices as well as users with 3D printing experience before physical products are produced through the cloud platform.This paper presents a 3D model to help users validate their designs and requirements not only in the traditional digital 3D model properties like shape and size,but also in physical material properties and manufacturing properties when producing physical products like surface roughness,print accuracy and part cost.These properties are closely related to the process of 3D printing and materials.In order to establish the 3D model,the paper analyzes the model of the 3D printing process selection in the cloud platform.Triangular intuitionistic fuzzy numbers are applied to generate a set of 3D printers with the same process and material.Based on the 3D printing process selection model,users can establish the 3D model and validate their designs and requirements on physical material properties and manufacturing properties before printing physical products.

Development of a 3D modeling algorithm for tunnel deformation monitoring based on terrestrial laser scanning

Deformation monitoring is vital for tunnel engineering.Traditional monitoring techniques measure only a few data points,which is insufficient to understand the deformation of the entire tunnel.Terrestrial Laser Scanning(TLS)is a newly developed technique that can collect thousands of data points in a few minutes,with promising applications to tunnel deformation monitoring.The raw point cloud collected from TLS cannot display tunnel deformation;therefore,a new 3D modeling algorithm was developed for this purpose.The 3D modeling algorithm includes modules for preprocessing the point cloud,extracting the tunnel axis,performing coordinate transformations,performing noise reduction and generating the 3D model.Measurement results from TLS were compared to the results of total station and numerical simulation,confirming the reliability of TLS for tunnel deformation monitoring.Finally,a case study of the Shanghai West Changjiang Road tunnel is introduced,where TLS was applied to measure shield tunnel deformation over multiple sections.Settlement,segment dislocation and cross section convergence were measured and visualized using the proposed 3D modeling algorithm.

3-D Lightning Location Solution and Precision Analysis of Cloud Flash

Using the spatial coordinates of detection stations and the time of arrival of lightning wave, the observation equations can be expressed. For the large lightning detection network, the least square method is used to process the adjustment of observation data to find the most probable value of lightning position, and the result is assessed by the mean error and dilution of precision. Lightning location precision is affected by figure factor. The conclusion can be used in the design of location network, data processing, and data analysis.

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.

MIXED-PHASE STRATIFORM CLOUD SYSTEM MODEL AND CASE MODELING ON TWO LOW-LEVEL MESOSCALE VORTICES

We introduced the two-parameter stratiform cloud model of Hu and Yan (1986) into the mesoscale model ofAnthes et al. (1987), and reprogramed the latter, then constructed a three-dimensional stratiform cloud system modelwhich includes three phases of water and detailed cloud physical processes. For the stability and accuracy of calculationin a larger time step, we accepted a set of hybrid-schemes for all and the time split scheme for some of the cloud physicalprocesses, and proposed a parameterized method which calculates different types of phase change processessimultaneously, and designed the falling schemes of particles following the Lagrangian method.We used a dry model, a cumulus parameterization model, a two-phase explicit scheme model, and the model pres-ented here to simulate two low-level mesoscale vortices, compared and analysed the simulating capability of these mod-els. The results show that in simulation of the circulation structure of meso-vortex, the structure of cloud system, andsurface precipitation, the model presented here is more reasonable and closer to the observations than other models.

A novel method for extracting skeleton of fruit treefrom 3D point clouds

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree.The phenomenon of organs’mutual occlusion in fruit tree canopy is usually very serious,this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree.However,traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points.To overcome this limitation,we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds.The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction,then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction.The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

强风暴电过程对霰粒子含量和谱分布影响的数值模拟研究

利用建立的耦合电过程三维冰粒子分档模式(通过引入电场力来考虑电场对粒子的影响),模拟研究了北京一次强雷暴发展过程中电过程对霰粒子含量、数浓度的影响。结果发现:(1)相对小的霰粒子含量受电过程直接影响较大,这种影响累积后,会对相对较大的霰粒子含量产生间接作用。在冰雹发展的初期和成熟期的部分阶段,电场对霰粒子最大含量所处空间位置基本没有影响。而在冰雹发展的成熟期向衰败期过渡时的部分时刻,电场对其稍有影响。在霰粒子最大含量处,直径相对较大的霰粒子决定着总的霰粒子含量。(2)总体来说,直径较小的霰粒子数浓度受电场影响较大,直径较大的霰粒子数浓度受电场影响较小。由于霰粒子含量中心大直径粒子较多,而其受电场的影响相对较小,并且该处电场也小于电场极值,故其最大含量受电场影响相对较小。所以,在此次个例的模拟过程中,霰粒子最大含量的时变曲线变化很小。(3)考虑电过程情况下,在霰粒子数浓度最大处,小直径霰粒子数浓度要么增加,要么略微减少,而大直径霰粒子要么进档增长受阻,要么数浓度减少。对不同直径的霰粒子来说,电过程既有可能使其数浓度增加,又有可能使其数浓度减少。当电场较大时,电过程对小直径霰粒子的影响比较直接,而对大直径霰粒子的影响相对间接;当电场较小时,电过程对霰粒子的谱分布影响相对较小。

强雷暴个例云内闪电与上升气流及液水含量关系的三维数值模拟

利用建立的三维闪电数值模式,模拟研究了北京2001年8月23日一次强雷暴发展过程中的云内闪电通道特征及其与上升气流和液水含量(LWC)之间的关系。结果发现:在强雷暴发展过程中,由于雪晶往往在上升气流相对较弱及LWC较低的地方形成、发展,与霰粒子之间的非感应起电过程首先发生在这些区域,然后发生电荷分离。因此,云内闪电往往在上升气流较弱和LWC相对较低的区域触发。闪电触发后,上行先导延伸区域的LWC较小,而下行先导延伸区的LWC取决于强风暴云发展的阶段。强风暴成熟期发生的闪电,下行先导可以延伸到较大LWC区,而无法延伸到LWC最大区。强雷暴衰退期发生的闪电,下行先导可以延伸到LWC最大区。