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 so...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.展开更多
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, clo...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.展开更多
Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minute...Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications.展开更多
In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation me...In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation method is proposed to mesh the point cloud data as a triangulation irregular network.Based on the geometric topology location information among radar point cloud data,focusing on the position relationship between adjacent scanning line of the point data,a preliminary match network is obtained according to their geometric relationship.A reasonable triangulation network for the object surface is acquired after the use of local optimization on initial mesh by Delaunay rule.Meanwhile,a new judging rule is proposed to contrast the triangulation before and after the optimization on the network.The result shows that triangulation for point cloud with full use of its own characteristics can improve the speed of the algorithm obviously,and the rule for judging the triangulation can evaluate the quality of network.展开更多
三维激光扫描技术作为一种先进的测量手段应用前景十分广阔,但是,在应用其扫描所得的点云数据进行内处理上又遇到了许多技术性的问题。CAD系统处理大量点云数据过程中存在局限性,一旦用CAD系统处理点云时,CAD程序就会出现错误操作提示,...三维激光扫描技术作为一种先进的测量手段应用前景十分广阔,但是,在应用其扫描所得的点云数据进行内处理上又遇到了许多技术性的问题。CAD系统处理大量点云数据过程中存在局限性,一旦用CAD系统处理点云时,CAD程序就会出现错误操作提示,甚至完全停止进程。CloudWorx克服了这些局限性,它避免了直接输入数据,而是利用Cyclone技术作为MicroStation环境下有效管理和解决点云的工具,使MicroStation内点云操作与CAD程序执行再无冲突。本文主要介绍了并且还介绍了CloudWorx模块的功能,并举例就点云数据在CloudWorx for MicroStation软件环境下的处理工作进行了详细的介绍。展开更多
Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between le...Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood（Cinnamomum camphora） using TLS point cloud data.First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.展开更多
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(3DM...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.展开更多
Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system.As a result,denoising is an essential procedure in processing point cloud d...Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system.As a result,denoising is an essential procedure in processing point cloud data for more accurate surface reconstruction and grain volume calculation.A classified denoising method was presented in this research for noise removal from point cloud data of the grain bulk surface.Based on the distribution characteristics of cloud point data,the noisy points were divided into three types:The first and second types of the noisy points were either sparse points or small point cloud data deviating and suspending from the main point cloud data,which could be deleted directly by a grid method;the third type of the noisy points was mixed with the main body of point cloud data,which were most difficult to distinguish.The point cloud data with those noisy points were projected into a horizontal plane.An image denoising method,discrete wavelet threshold(DWT)method,was applied to delete the third type of the noisy points.Three kinds of denoising methods including average filtering method,median filtering method and DWT method were applied respectively and compared for denoising the point cloud data.Experimental results show that the proposed method remains the most of the details and obtains the lowest average value of RMSE(Root Mean Square Error,0.219)as well as the lowest relative error of grain volume(0.086%)compared with the other two methods.Furthermore,the proposed denoising method could not only achieve the aim of removing noisy points,but also improve self-adaptive ability according to the characteristics of point cloud data of grain bulk surface.The results from this research also indicate that the proposed method is effective for denoising noisy points and provides more accurate data for calculating grain volume.展开更多
Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in dif...Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.展开更多
基金supported by the Innovation and Entrepreneurship Training Program Topic for College Students of North China University of Technology in 2023.
文摘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.
基金Project(51274250)supported by the National Natural Science Foundation of ChinaProject(2012BAK09B02-05)supported by the National Key Technology R&D Program during the 12th Five-year Plan of China
文摘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.
基金National Natural Science Foundation of China(No.41801379)Fundamental Research Funds for the Central Universities(No.2019B08414)National Key R&D Program of China(No.2016YFC0401801)。
文摘Tunnel deformation monitoring is a crucial task to evaluate tunnel stability during the metro operation period.Terrestrial Laser Scanning(TLS)can collect high density and high accuracy point cloud data in a few minutes as an innovation technique,which provides promising applications in tunnel deformation monitoring.Here,an efficient method for extracting tunnel cross-sections and convergence analysis using dense TLS point cloud data is proposed.First,the tunnel orientation is determined using principal component analysis(PCA)in the Euclidean plane.Two control points are introduced to detect and remove the unsuitable points by using point cloud division and then the ground points are removed by defining an elevation value width of 0.5 m.Next,a z-score method is introduced to detect and remove the outlies.Because the tunnel cross-section’s standard shape is round,the circle fitting is implemented using the least-squares method.Afterward,the convergence analysis is made at the angles of 0°,30°and 150°.The proposed approach’s feasibility is tested on a TLS point cloud of a Nanjing subway tunnel acquired using a FARO X330 laser scanner.The results indicate that the proposed methodology achieves an overall accuracy of 1.34 mm,which is also in agreement with the measurements acquired by a total station instrument.The proposed methodology provides new insights and references for the applications of TLS in tunnel deformation monitoring,which can also be extended to other engineering applications.
基金the National Natural Science Foundation of China (No. 50805094)the National Basic Research Program (973) of China (No. 2006CB705400)
文摘In order to study the triangulation for the point cloud data collected by three-dimensional laser radar,in accordance with the line-by-line characteristics of laser radar scanning,an improved Delaunay triangulation method is proposed to mesh the point cloud data as a triangulation irregular network.Based on the geometric topology location information among radar point cloud data,focusing on the position relationship between adjacent scanning line of the point data,a preliminary match network is obtained according to their geometric relationship.A reasonable triangulation network for the object surface is acquired after the use of local optimization on initial mesh by Delaunay rule.Meanwhile,a new judging rule is proposed to contrast the triangulation before and after the optimization on the network.The result shows that triangulation for point cloud with full use of its own characteristics can improve the speed of the algorithm obviously,and the rule for judging the triangulation can evaluate the quality of network.
文摘三维激光扫描技术作为一种先进的测量手段应用前景十分广阔,但是,在应用其扫描所得的点云数据进行内处理上又遇到了许多技术性的问题。CAD系统处理大量点云数据过程中存在局限性,一旦用CAD系统处理点云时,CAD程序就会出现错误操作提示,甚至完全停止进程。CloudWorx克服了这些局限性,它避免了直接输入数据,而是利用Cyclone技术作为MicroStation环境下有效管理和解决点云的工具,使MicroStation内点云操作与CAD程序执行再无冲突。本文主要介绍了并且还介绍了CloudWorx模块的功能,并举例就点云数据在CloudWorx for MicroStation软件环境下的处理工作进行了详细的介绍。
文摘Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood（Cinnamomum camphora） using TLS point cloud data.First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.
基金funded by the National Natural Science Foundation of China(32371985)the Fundamental Research Funds for the Central Universities,China(226-2022-00217).
文摘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.
基金National Natural Science Foundation of China(No.50975121)Jilin Province Science and Technology Development Plan Item(No.20130522150JH)2013 Jilin Province Science Foundation for Post Doctorate Research(No.RB201361).
文摘Surfaces of stored grain bulk are often reconstructed from organized point sets with noise by 3-D laser scanner in an online measuring system.As a result,denoising is an essential procedure in processing point cloud data for more accurate surface reconstruction and grain volume calculation.A classified denoising method was presented in this research for noise removal from point cloud data of the grain bulk surface.Based on the distribution characteristics of cloud point data,the noisy points were divided into three types:The first and second types of the noisy points were either sparse points or small point cloud data deviating and suspending from the main point cloud data,which could be deleted directly by a grid method;the third type of the noisy points was mixed with the main body of point cloud data,which were most difficult to distinguish.The point cloud data with those noisy points were projected into a horizontal plane.An image denoising method,discrete wavelet threshold(DWT)method,was applied to delete the third type of the noisy points.Three kinds of denoising methods including average filtering method,median filtering method and DWT method were applied respectively and compared for denoising the point cloud data.Experimental results show that the proposed method remains the most of the details and obtains the lowest average value of RMSE(Root Mean Square Error,0.219)as well as the lowest relative error of grain volume(0.086%)compared with the other two methods.Furthermore,the proposed denoising method could not only achieve the aim of removing noisy points,but also improve self-adaptive ability according to the characteristics of point cloud data of grain bulk surface.The results from this research also indicate that the proposed method is effective for denoising noisy points and provides more accurate data for calculating grain volume.
文摘Multi-view laser radar （ladar） data registration in obscure environments is an important research field of obscured target detection from air to ground. There are few overlap regions of the observational data in different views because of the occluder, so the multi-view data registration is rather difficult. Through indepth analyses of the typical methods and problems, it is obtained that the sequence registration is more appropriate, but needs to improve the registration accuracy. On this basis, a multi-view data registration algorithm based on aggregating the adjacent frames, which are already registered, is proposed. It increases the overlap region between the pending registration frames by aggregation and further improves the registration accuracy. The experiment results show that the proposed algorithm can effectively register the multi-view ladar data in the obscure environment, and it also has a greater robustness and a higher registration accuracy compared with the sequence registration under the condition of equivalent operating efficiency.
