点云数据与STL格式转换 PPT
点云数据转换成实体模型通过基于点的立体像素化立体像素
点云数据转换成实体模型通过基于点的立体像素化立体像素PointCloudDataConversionintoSolidModelsviaPoint-BasedVoxelization1 2 3 4Tommy Hinks ; Hamish Carr ; Linh Truong-Hong ; and Debra F. Laefer, M.ASCEAbstract:Automatedconversionofpointclouddatafromlaserscanninginto formatsappropriateforstructuralengineeringholdsgreatprom- iseforexploitingincreasinglyavailableaeriallyandterrestriallybase dpixelizeddataforawiderangeofsurveying-relatedapplicationsfrom environmental modeling to disaster management. This paper introduces a point-based voxelization method to automatically transform pointclouddataintosolidmodelsforcomputationalmodeling.Thefundamentalvi abilityofthetechniqueisvisuallydemonstratedforbothaerial andterrestrialdata.Foraerialandterrestrialdata,thiswasachievedinl essthan30sfordatasetsupto650,000points.Inallcases,thesolid models converged without any user intervention when processed in a commercial ?nite-element method program. DOI: 10.1061/ASCESU.1943-5428.0000097 2013 American Society of Civil Engineers.CE Database subject headings: Data processing; Surveys; Finite element method; Information management.Author keywords: Terrestrial; Aerial; Laser scanning; LiDAR; Voxelization; Computational modeling; Solid models; Finite element.Introductionexist.Thispaperlaysthegroundworkforkeyadvancementinsucha pipeline. The procedure proposed herein to reconstruct buildingLaser scanning has achieved great prominence within the civil en- facadesfrompointcloud,whichisafundamentalstepforgenerating gineering community in recent years for topics as divergent as city-scale computational models.coastline monitoring Olsen et al. 2009, 2011, airport layout op- timization Parrish and Nowak 2009, and ground-displacementidenti?cation for water-system risk assessment Stewart et al.FacadeReconstruction2009. Additionally, there has been strong motivation to obtainfurther functionality from laser scanning and other remote-sensing Inrecentyears,developmentsinlaser-scanningtechnologyand?ight-data, including three-dimensional 3D volume estimation forpath planning have allowed aerial laser scanning ALS to acquire mining Mukherji 2012, road documentation Dong et al. 2007,pointclouddataquicklyandaccuratelyatacityscale,therebyhaving structuralidenti?cationShanandLee2005;Zhangetal.2012,and thepotentialforreconstructing3Dbuildingsurfacesacrossanentire emergency planning Laefer and Pradhan 2006. Furthermore,city in nearly real time. A number of approaches based on semi- computational responses of city-scale building groups are increas- automaticLangandForstner1996andautomaticHenricssonetal. inglyindemandforheightenedurbanization,disastermanagement,1996techniqueshavebeenproposedtoreconstructbuildingmodelsand microclimate modeling, but input data are typically too ex- from such data sets, but automatically extracting highly detailed, pensive as a result of the need for manual surveying. Additionally, accurate,andcomplexbuildingsstillremainsachallengeHaalaandcurrent software tools for transforming remote-sensing data into Kada 2010. The semiautomatic procedures need human operator computationalmodelshaveoneormoreofthefollowingproblems: intelligence.TheautomaticvisualmodelingofurbanareasfromALS alowdegreeofreliability,aninabilitytocapturepotentiallycritical data tends to extract sample points for an individual building by details,and/oraneedforahighdegreeofhumaninteraction.Todate, applying segmentation techniques and then reconstructing eacha seamless, automated, and robust transformation pipeline frombuilding individually. In such cases, vertical facade surfaces are notremote-sensing data into city-scale computational models does not portrayed in detail, and outlines may be of relatively low accuracy unless ground planes are integrated, which requires either a priori1 informationormanualintervention.Unfortunately,theeffectivenessDoctoralRecipient,SchoolofComputerScienceandInformatics,Univ.of engineering modeling often depends largely on the geometricCollege Dublin, Bel?eld, Dublin 4, Ireland. E-mail: ******************2accuracy and details of the building models?thus the currentSeniorLecturer,SchoolofComputing,FacultyofEngineering,Univ.ofmismatch.Leeds, Leeds LS2 9JT, U.K. E-mail: h.carr@//0>.3Post-doctoral Researcher, Urban Modelling Group, School of Civil, Presently, commercial products are generally semiautomatic StructuralandEnvironmentalEngineering,Univ.CollegeDublin,Bel?eld, Laefer et al. 2011, whereas in the computer graphics and photo- Dublin 4, Ireland. E-mail: linh.truonghong@gmailgrammetry communities, researchers have focused on automated4AssociateProfessor,LeadPI,UrbanModellingGroup,SchoolofCivil,surfacereconstructionfromdenseandregularsamplepointsHoppeStructuralandEnvironmentalEngineering,Univ.CollegeDublin,Bel?eld, 1994; Kazhdan et al. 2006. Unfortunately, ALS data are oftenDublin 4, Ireland corresponding author. E-mail: ******************* sparse and irregular, and may contain major occlusions on vertical Note.ThismanuscriptwassubmittedonNovember16,2011; approvedsurfaces owing to street- and self-shadowing Hinks et al. 2009.on September 10, 2012; published online on September 13, 2012. Discus- Dedicated urban modeling surface-reconstruction approachession period open until October 1, 2013; separate discussions must be generallyusethemajorbuildingplanesChenandChen2007andsubmitted for individual papers. This paper is part of the Journal ofcan be described as either model-driven or data-driven. Model-Surveying Engineering, Vol. 139, No. 2, May 1, 2013ASCE, ISSN0733-9453/2013/2-72?83/$25.00. driven techniques use a ?xed set of geometric primitives that are72 / JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13.Copyright ASCE. For personal use only; all rights reserved.Fig. 1. Work?ow of the proposed approach: *Collection and preparation of LiDAR data involve multiple steps outside the scope of this paper’s scienti?ccontribution;thesegenerallyincludeplanning,collection,re gistration,and?ltering;seeTruong-Hong2011andHinks2011forfurther detailsttedtothepointdata.Suchtechniquescanbeeffectivewhenadataset is sparse because the ?tting of geometric primitives does not require complete data. In contrast, data-driven techniques derive surfaces directly from the point data and are capable of modeling arbitrarilyshapedbuildings.Generally,data-drivenapproachesaremore?exiblethanmodel-drivenapproaches,butareoftensensitiveto noise in the input data.For strictly visual representation, model-driven approachescanbeeffective.Forexample,Haalaetal.1998 proposed four dif-ferent primitives and their combinations to automatically derive 3D building geometry of houses from ALS and existing ground planes.Similarly, Maas and Vosselman 1999 introduced an invariantmoment-basedalgorithmfortheparametersofastandardgabled-roofhouse type that allowed for modeling asymmetric elements such as dormers. However, these efforts assume homogeneous point dis-Fig. 2. Octree representationtributions, which is unrealistic. You et al. 2003 also adapted a set of geometric primitives and ?tting strategies to model complex buildings with irregular shapes, but the approach required user interventionandgeneratedonlylimitedwalldetails.Huetal.2004used a combination of linear and nonlinear ?tting primitives to SolidModelingreconstructacomplexbuilding,inwhichaerialimagerywasusedtore?ne the models. To generate building models directly from point cloud data forIncontrast,manydata-driventechniquesoperatingonALSdata engineering simulations [e.g., FEM], there are three dominant reconstruct roof shapes directly from sample points of roof planes. methods:1constructivesolidgeometryCSG,whereobjectsareSubsequently, the remainder of the building is simply extruded represented using Boolean combinations of simpler objects; 2 to the ground level from the roof-shape outlines. Vosselman and boundary representations B-reps, where object surfaces are rep- Dijkman2001usedaHoughtransformforextractionofplanefaces resentedeitherexplicitly orimplicitly;and3spatialsubdivision roofplanesfromtheALSdata,andthen3Dbuildingmodelswere representations,wherean objectdomain is decomposed intocells reconstructed by combining ground planes and the detected roof withsimple topologic and geometric structure, such as regular planes.Hofmannetal.2003introducedamethodtoextractplanar gridsandoctreesGoldman2009;HoffmannandRossignac1996;roof faces by analyzing triangle mesh slopes and orientations from there are many extensive treatises available for in-depth consid-a triangular irregular network structure generated from ALS data. eration of this topic B?hm et al. 1984; Rossignac and Requicha More recently, Dorninger and Pfeifer 2008 used an a-shape ap- 1984, 1999.proach to determine a roof outline from point clouds of the roof Generating solid models automatically from point cloud data projectedontoahorizontalplane.Also,ZhouandNeumann2010 is particularly important because the cost of manually creating created impressive buildings for a large urban area by using a vol- solid models of existing objects is far greater than the associated umetric modeling approach in which roof planes were determined hardware,software,andtrainingcosts.Assuch,spatialsubdivision based on a normal vector obtained from analysis of grid cells be- representations are used extensively for creating solid models of longingtorooflayers.However,thesemodelsarealsoextrudedand buildings in which regular grids or octrees are employed to de- lack vertical-wall details. compose an entire object intononoverlapping 3D regions, com-Therefore, this paper presents an automated approach to con- monly referred to as voxels. Voxels are usually connected andverting point clouds of individual buildings into solid models for described a simple topologic and geometric structure. In grids, structural analysis by means of computational analysis in which avolumeissubdividedintosmallerregionsbyappropriateplanes thepointcloudthatweresemiautomaticallysegmentedfromLight parallel to the coordinate system axes,typically using aCartesian Detection and Ranging LiDAR data become the input Fig. 1. coordinate system. An initial voxel bounding all point data re-Notably, this proposed approach focuses on reconstructing solid cursively divides a volume into eight subvoxels, organized in modelsbyusingvoxelgridswiththecriticalparameteraseitherthe a hierarchical structure Samet 1989. Voxels may be labeled voxel size or the number of voxel grids; for more details on col- white,black,orgraybasedontheirpositionsFig.2.Blackvoxels lecting ALS and terrestrial laser scanning TLS data and on are completely inside the solid, whereas white voxels are com- segmenting point clouds, see Truong-Hong 2011andHinks pletelyoutside.Voxelswithbothblackandwhitechildrenaregray 2011. Hoffmann and Rossignac 1996.JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013 / 73J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13. Copyright ASCE. For personal use only; all rights reserved.Fig.3.Voxelgridspanningavolumeina3Dspaceboundedbyx ,x ,y ,y ,andz ,z ,whe reDx,Dy,andDzarevoxelsizes andmin min minN , N , and N are the number of voxels in each directionx y zIn an application of spatial subdivision for surface recon-struction,CurlessandLevoy1996presentedavolumetricmethodforintegratingrangeimagestoreconstruc tanobject’ssurfacebasedon acumulative weighted signed-distancefunction. Unfortunately,the approach is not suited for arbitrary objects. In related work, GuarnieriandPontin2005builtatriangulatedmeshofanobject’ssurfacebycombiningaconsensussurface[asproposedbyWheeleret al. 1998], an octree representation, and the marching-cubesalgorithm Lorensen and Cline 1987. This multifaceted algorithmFig. 4. Point-based voxelization avoids surface reconstruction and canreducetheeffectofthenoiseowingtosurfacesampling,sensoroperates directly on point datameasurements,andregistrationerrors.However,foroptimalresults,themethodrequiresmodi?cationofparametersthatdependheavilyon input-data characteristics such as the voxel size, the threshold value for the angle, and the distance between two consecutive neighbor-range viewpoints. z 2zminN? 1 ?3?zDzThevoxelhaseightlatticeverticesassociatedwithsixrectangular VoxelizationfacesFig.3.Eachinteriorvoxelhas26neighboringvoxels,witheight sharing a vertex,12 sharing an edge,and six sharing a face. Critical to octree/quadree representations for further processing is Conversely,anexteriororinteriorvoxelonahole’sboundaryoften voxelization. This term describes the conversion of any type of has only 17 neighboring voxels four sharing a vertex, eight geometric or volumetric object such as a curve, surface, solid, or sharinganedge,and?vesharingaface.Moreover,mostexisting computedtomographicdataintovolumetricdatastoredina3Darray voxelization techniques operate on surface representations ofof voxels Karabassi et al. 1999. Initially, a voxel grid divides objects, where a signi?cant part of the problem is to identifya bounded 3D region into a set of cells, which are referred to as throughwhichvoxelsthesurfacespass.Suchmethodsarereferredvoxels. The division is typically conducted in the axial directions to as surface-based voxelization Cohen-Or and Kaufman 1995of a Cartesian coordinate system. Before voxelization, three pairs [Fig.4a?c].Incontrast,thepoint-basedvoxelizationinthispaper ofcoordinatevalues??x , x , ?y , y , and ?z , z ? aremin min minoperates directly on the point data and does not require a derived createdalongthethreeaxesX, Y, and Zde?ningaglobalsystemsurface [Fig. 4a?c]. Point-based voxelization is conceptually Fig. 3. The basic idea of a voxelization algorithm is to examine much simpler than surface-based voxelization algorithms, and whethervoxelsbelongtotheobjectofinterestandtoassignavalue whereas the mechanisms are well known, they have not beenof 1 or 0,respectively Karabassi et al. 1999; a further description applied to generating solid modeling of buildings from LiDARof voxel grids is available in Cohen and Kaufman 1990.data.An initial voxel bounding all point cloud data in 3D Euclidean3Asmentionedearlier,eachvoxelisclassi?edasactiveorinactivespaceR is subdivided into subset voxels by grids along the x-, y-, corresponding to binary values based on the sample points within andz-coordinatesinaCartesiancoordinatesystem.Eachvoxelinthethat voxel [Eq. 4]subset is represented by an index v?i, j, k?, where i2?0; N 21 , xj2?0; N 21 , and k2?0; N 21 Fig. 3. With the dimensionsy zactive ifn$TnofindividualvoxelsDx, Dy, Dz,anumberofvoxelsN , N , Nx y zf n?4?valong each direction are given in Eqs. 1?3 inactive ifn,Tnwheretheargumentn5numberofpointsmapping to avoxel,andx 2xmin T 5user-speci?edthresholdvalue.Typically,T 51,whichmeansn nN? 1 ?1?xDxthat voxels containing at least one mapping point are classi?edasactiveandallothersasinactive.Moresophisticateddensity-basedy 2yminclassi?cation functions can be designed. An example is shown inN? 1 ?2?yDyFig. 5.74 / JOURNALOFSURVEYINGENGINEERING?ASCE / MAY2013J. Surv. Eng. 2013.139:72-83.Downloaded from by East China Inst of Tech on 04/13/13. Copyright ASCE. For personal use only; all rights reserved.Fig. 5. Voxelization model of front building of Trinity College, Dublin, Ireland, created by a voxel grid: a input data set of 245,000 ALS points;bvoxelizationmodelwithvoxelsizeDx5Dy5Dz50:25m;cvoxelclassi?cationwiththethresholdT51andvoxelizationmodelwithaboutn5,000 active voxels n is the largest number of points mapping to asingle voxelFig. 6. Solid model componentsProposedConversionofVoxelizedModelsintoSolidModelsTo reconstruct vertical surfaces of building models, a voxel grid is used to divide data points in a bounded 3D region into smallervoxels. Important facade features such as windows and doors are subsequently detected basedon a voxel’s characteristics, where an inactive voxel represents the inside of an opening. Consequently, building models are converted into an appropriate format for com- putational processing.Anobjectisde?nedbyitssurfaceboundary,whichthenmustbeFig. 7. Face orientation as dictated by the right-hand ruleconvertedintoanappropriatesolidrepresentationcompatiblewithcommercialcomputationalpackages.Althoughmanyschemesareavailable,B-repsarehereinadoptedbecauseoftheircompatibilitywith commercial structural-analysis software e.g., ANSYS soft- Keypointsarerepresentedbya3Dcoordinateofasingularpoint.ware Laefer et al. 2011. The proposed method de?nes both the An edge is de?ned as the connection between exactly two keygeometry and topology of an object by a set of nonoverlappingpoints;forexample,theedgee 5fP, Pgistheedgewithstartingij i jandendingpointP.Notably,edgeshaveanorientation;asfaces approximate the boundary of the solid model. This section pointPi jsuch, e 52eThus, the edges e and e would be ?ipped. EdgepresentsabriefdescriptionoftheB-repschemeimplementedintheij ji ij jiproposed approach; for more details, see Goldman 2009. Ge- ?ipping is important when de?ning an orientable face for dis-ometry is de?ned by key singular points, with each point rep- tinguishing the inside from the outside.resenting a speci?c location in space. Topology is de?ned by Similarly, faces represent surfaces of a solid model that areconnections between key points. When used together, they can connections between edges. The faces are further connected de?neasolidmodelFig.6.DatastructuresfordescribingB-reps to form volumes. A face is de?ned as a list of edgesoften capture the incidence relations between a face and its f5fe ,e ,.,e g that involve closed paths. A face01 12 ?n22??n21?bounding edges and an edge and its bounding vertices, whichconsistingofthreekeypointsisatriangle,whereasqu。
Polyworks的主要模组和功能介ppt课件
面，线，点 基准的权重
点云上的特征 3D档上的特征
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对比(Compare):
对齐完后，将点云和标准档进行对比，生成颜色比对报告。
对比的工具条
最大距离 最大角度
对比的方向，一般 选择最近。
累计比较结果，仅用于 检视模块。
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色谱栏的设置：
由此进入色谱栏设置
转换颜色：此 处有三个选项， 自动范围，用 户自定义，使 用特征公差， 一般根据自己 的需要进行选 择。
纵向 横向
進入屬性設置后，可以將所版 面設置成橫向的或者縱向的。
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縮減公差
光順水平，有高，中，低三種 網格最大距離 面取樣的距離 標準偏差
光順
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因目前我们的KEY文件不支持点云的编辑模块，所以当勾选计算完后再编 辑模块打开点云时会弹出找不到KEY文件，这时候直接点”OK”按钮即可， 软体即回到主界面。
将计算后的点云选中， 点右键，可将计算出 来的点云汇出来！
最佳拟合
自动初始猜测。 最大距离 数据采集的比例
次数 收敛值
当自动初始猜测不勾选时，可以限制几个方向的 自由度，如下页图所示：
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最佳拟合对齐（Best Fit）：
设置几个方向的自 由度。
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两个面，两条轴，两个中心点：
将所需要做为基准的面，线，轴都创建出来后，就可以以之为基准，来 做基准对齐，其实就是3-2-1法的对齐。
1.Polyworks软体界面介绍； 2. 计算STL点云的实例介绍对齐和计算模块； 3.CAV检测模块； 4.尺寸测量及形位公差评价；
3
4
双击
桌面图标进入Polyworks软体界面，如下图所示： 点击相应的图标进入相应的功能模块
点云数据文件常用格式
点云数据⽂件常⽤格式⽂件类型汇总OFF - Object File FormatPLY - Polygon File Format also known as the Stanford Triangle FormatPTS - Laser scan data formatPTX - ASCII based interchange format for point cloud dataXYZ - Based on Cartesian coordinatesLAS/LAZ - The most common format for exchanging points clouds⽂件格式介绍1. OFF - Object File FormatOFF numVertices numFaces numEdgesx y zx y z... numVertices like aboveNVertices v1 v2 v3 ... vNMVertices v1 v2 v3 ... vM... numFaces like above⽴⽅体使⽤ OFF 格式描述的例⼦:OFF8 6 0-0.500000 -0.500000 0.5000000.500000 -0.500000 0.500000-0.500000 0.500000 0.5000000.500000 0.500000 0.500000-0.500000 0.500000 -0.5000000.500000 0.500000 -0.500000-0.500000 -0.500000 -0.5000000.500000 -0.500000 -0.5000004 0 1 3 24 2 35 44 45 7 64 6 7 1 04 1 75 34 6 0 2 42. PLY - Polygon File Format also known as the Stanford Triangle Formatplyformat ascii 1.0 { ascii/binary, format version number }comment made by Greg Turk { comments keyword specified, like all lines }comment this file is a cubeelement vertex 8 { define "vertex" element, 8 of them in file }property float x { vertex contains float "x" coordinate }property float y { y coordinate is also a vertex property }property float z { z coordinate, too }element face 6 { there are 6 "face" elements in the file }property list uchar int vertex_index { "vertex_indices" is a list of ints }end_header { delimits the end of the header }0 0 0 { start of vertex list }0 0 10 1 10 1 01 0 01 0 11 1 11 1 04 0 1 2 3 { start of face list }4 7 65 44 0 45 14 156 24 2 6 7 34 3 7 4 03. PTS - Laser scan data format253730194-0.41025 -2.0806 8.00981 55 52 44 65-0.63016 -1.84527 6.59447 228 228 230 225-0.4766 -2.14446 7.91288 60 56 54 68-0.52017 -1.51698 7.91458 60 58 50 71: : :: : :: : :PTS ⽂件⽆法⽤ MeshLab 直接打开，⽽ PLY ⽂件可以，所以可以⼿动将 PTS ⽂件转换为 PLY ⽂件，便可以在 MeshLab 中查看。
点云编码标准
点云编码标准
点云编码标准是指在三维计算机视觉领域中，对点云数据进行编码的一种规范化标准。
点云数据是由大量从物体表面采集的三维坐标点组成的，这种数据结构在计算机视觉、机器人视觉、虚拟现实、游戏等领域有着广泛的应用。
点云编码标准主要包括点云数据的格式和压缩算法两个方面。
在点云数据格式方面，主要有PLY、OBJ、STL、PCD等格式，每种格式都有其特点和应用场景。
在点云压缩算法方面，主要有基于无损压缩的算法和基于有损压缩的算法两种，其中基于无损压缩的算法包括LASzip、Entwine Point Tile等，而基于有损压缩的算法包括简化算法、网格化算法等。
点云编码标准的制定可以使得不同系统之间的点云数据传输更
加便捷，同时也可以提高点云数据的存储效率和处理速度。
随着智能制造、智能交通等领域的不断发展，点云数据的应用将会越来越广泛，点云编码标准也将会不断完善和更新。
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solidworks点云生成曲面步骤
solidworks点云生成曲面步骤SolidWorks点云生成曲面步骤SolidWorks是一款常用的三维CAD软件，可以用于建模、装配和绘图等。
在使用SolidWorks进行建模时，我们经常需要将实际物体的点云数据转化为CAD模型。
本文将介绍如何使用SolidWorks生成曲面，以便更加准确地模拟实际物体。
一、什么是点云？点云是指由大量离散的点组成的三维空间数据集合。
在实际应用中，我们可以通过激光扫描、摄像头拍摄等方式获取物体表面上的点云数据，并将其转化为CAD模型。
二、如何导入点云数据？1.打开SolidWorks软件，在顶部菜单栏中选择“文件”-“打开”。
2.在弹出的对话框中选择“所有文件”类型，找到并选择要导入的点云数据文件。
3.点击“打开”按钮即可导入点云数据。
三、如何生成曲面？1.选择“曲面”工具栏，在下拉菜单中选择“曲面网格”。
2.在弹出的对话框中，选择导入的点云数据文件，并设置网格密度和最大边界长度等参数。
3.点击“确定”按钮开始生成曲面网格。
这个过程可能需要一些时间，请耐心等待。
4.生成曲面网格后，我们可以对曲面进行调整和优化。
例如，可以使用“修复曲面”工具来消除网格中的不规则形状和孔洞。
5.调整完毕后，我们可以将曲面导出为SolidWorks支持的格式，例如STL或IGES等。
四、如何优化曲面？1.选择“曲面”工具栏，在下拉菜单中选择“修复曲面”。
2.在弹出的对话框中，选择要修复的曲面，并设置修复参数。
例如，可以设置最大距离和最大角度等参数来控制修复程度。
3.点击“确定”按钮开始修复曲面。
这个过程可能需要一些时间，请耐心等待。
4.修复完成后，我们可以使用“检查几何体”工具来检查曲面的质量。
如果发现问题，可以再次使用“修复曲面”工具进行调整。
五、如何导出CAD模型？1.选择“文件”-“另存为”，在弹出的对话框中选择要导出的格式（例如STL或IGES）。
2.设置导出参数，并点击“确定”按钮开始导出CAD模型。
激光点云数据格式标准
激光点云数据格式标准
激光点云数据格式标准是一种将三维空间中的点以坐标形式进行存储的文件格式，用于激光雷达、三维扫描仪等设备采集的数据。
激光点云数据格式标准有多种，其中常见的有ASCII格式和二进制格式。
ASCII格式以文本形式表示点云数据，每个点以一行为单位，包括点的坐标和可能的属性信息，如颜色、反射率等。
这种格式的优点是易于阅读和编辑，但缺点是文件较大，存储和传输效率较低。
二进制格式则将点云数据以二进制形式进行存储，通常使用压缩算法来减小文件大小。
二进制格式的优点是存储和传输效率高，但缺点是不易于阅读和编辑。
在实际应用中，激光点云数据通常会根据不同的应用场景和数据特点选择不同的格式标准。
例如，对于大规模的点云数据，可能会选择使用分块的方式进行存储和传输，以提高处理效率。
同时，随着技术的不断发展，新的激光点云数据格式标准也在不断涌现，如Open3D等开源库提供了多种格式转
换和处理的工具，为激光点云数据的处理和应用提供了更多的选择和便利。
总的来说，激光点云数据格式标准的发展是随着激光雷达、三维扫描仪等设备的普及和技术的不断进步而不断完善的。
未来，随着人工智能、机器学习等技术的进一步发展，激光点云数据格式标准的应用场景将更加广泛和深入，为智能感知、自动驾驶、机器人等领域的发展提供更加有力的技术支持。
《点云配准方法》课件
深度学习算法能够自动提取点云特征，减少人工干预，提高配准精度和效率。
未来研究方向包括改进神经网络结构、优化训练算法以及提高网络泛化能力等。
高精度与实时性需求的挑战与解决方案
随着点云数据量的增加，高精度和实时性的需求对点云配准提出了更高 的要求。
点云配准的背景和重要性
背景
随着三维扫描技术的发展，点云数据在许多领域得到广泛应用，如逆向工程、 机器人导航、文物修复等。由于不同来源的点云数据存在差异，需要进行配准 以实现数据的整合和利用。
重要性
点云配准是实现多传感器融合、提高测量精度、扩展应用范围的关键技术之一 。
点云配准的基本流程
特征提取
相似性度量
采用高性能计算技术和优化算法，提高点云配准速度和精度，以满足实 时性和高精度需求。
未来研究重点包括开发高效的数据结构和算法，以及利用并行计算和 GPU加速等技术提高计算性能。
多模态点云数据的配准问题
多模态点云数据包括激光雷达点云、 RGB-D相机点云等，其配准问题具有
挑战性。
解决多模态点云数据配准问题需要研究 不同模态点云之间的特征提取和匹配方
医学影像处理
点云配准在医学影像处理中也有着重 要的应用价值。通过对医学影像数据 进行配准，能够实现更加精确的疾病 诊断和治疗方案制定。
点云配准在医学影像处理中主要用于 图像融合、病灶检测和手术导航等方 面，为医学影像分析提供更加准确和 可靠的技术手段。
05
点云配准的未来展望
深度学习在点云配准中的应用
基于刚性变换的点云配准
刚性变换：将源点云数据与目 标点云数据在空间中进行对齐 ，保持点云之间的相对位置和 方向不变。
点云存储文件格式简介
点云存储文件格式简介在众多存储点云的文件格式中，有些格式是为点云数据“量身打造”的，也有一些文件格式（如计算机图形学和计算机和学领域的3D 模型或通讯数据文件）具备表示和存储点云的能力，应用于点云信息的存储。
本文将这些文件格式一并视为“点云存储文件格式”。
从这个意义上说，除了包含基本的点的笛卡尔坐标信息之外，有些文件格式还可能包含点之间的连接关系（拓扑结构），以及法线等其他信息。
一、常见点云存储文件格式简介每个人都可以定义自己的数据存储格式，也因此产生了不计其数点云存储文件格式。
一些文件格式致力于标准化与通用性，而今被多个相关软件或软件库所支持，也被大多数业内人士所认同和使用。
下面按字母排列顺序简单介绍几种最常见的点云格式。
*.lasLiDAR数据的工业标准格式，旨在提供一种开放的格式标准，允许不同的硬件和软件提供商输出可互操作的统一格式。
是一种二进制文件格式。
LAS文件包含以下信息：l C--class(所属类)l F一flight(航线号)l T一time(GPS时间)l I一intensity(回波强度)l R一return(第几次回波)l N一number of return(回波次数)l A一scan angle(扫描角)l RGB一red green blue(RGB颜色值)示例：可以看出，las文件格式除了基本的三维坐标之外，保留了原始扫描的数据采集信息。
LAS格式定义中用到的数据类型遵循1999年ANSI(AmericanNationalStandardsInstitute,美国国家标准化协会)C语言标准。
参考资料：https:///chenbokai/p/6010143.html*.obj是由Alias|Wavefront Techonologies公司从几何学上定义的3D 模型文件格式，是一种文本文件。
通常用以“#”开头的注释行作为文件头。
数据部分每一行的开头关键字代表该行数据所表示的几何和模型元素，以空格做数据分隔符。
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STL格式介绍
stl文件是在计算机图形 学应用系统中，用于表 示三角形网格的一种文 件格式。
比如: 这只猫
STL格式优点
文件格式简单，应用广泛。 STL是最多快速原型系统（3D打印）所应用的标准文件类
型。 STL是用三角网格来表现3D CAD模型。
直接使用点云数据的缺点
三维激光扫描系统采集的点云数据可以在AutoCAD软件上 直接使用，但是点云数据量过大，个人电脑无法运行如此 海量数据。
先进行处理再转换
例如我们用 右面这张图 做演示：
ห้องสมุดไป่ตู้
转换
首先把剔除干扰点后的点云数据转换成纯文本（.txt）格 式
如右图：
转换
然后再把纯文本格式转换成CAD格式
海量数据处理方法
大家学习辛苦了，还是要坚持
继续保持安静
海量数据处理方法
2.在分接处设立三四个参考点（方便拼图）
海量数据处理方法
海量数据处理方法
4.最后将处理后的点云数据导出，转换成AutoCAD 格式的点云数据，经上述处理数据量会从100多万 降到3万，这样就可以在AutoCAD平台处理操作
逆向工程
工业上先做出油泥模型，再测量模型表面的点阵
数据，最后利用逆向工程软件进行处理。
似。
我们要做的利用knict采集数据和上述过程很相
Imageware surfacer
目前比较有名气的逆向工程软件就属这款Imageware公司 旗下的surfacer，可以直接将点云数据进行处理并转换为 stl格式的文件。
Imageware 12.1新功能以及改善：
Opening NX Parts in Imageware---可以直接打开NX模型 Importing an Image as a Point Cloud---直接输入一个图片作为点云处理 Using Custom Views--可以自定义视图 Layer Manager Enhancements--增强图层管理 Circle-Selecting Points--增加圈选点云 Global Model Clouds--全局模型点云 Global Model Surfaces--全局模型曲面 Feature-Based Alignment--基于特征的对齐方式