针对菇房内杏鲍菇表型参数测量任务中,由于扫描设备视角受限,扫描的杏鲍菇点云出现残缺问题,基于AdaPoinTr(Adaptive geometry-aware point transformers)提出了改进的SwinPoinTr模型,实现了对残缺杏鲍菇点云的准确补全和杏鲍菇表型参...针对菇房内杏鲍菇表型参数测量任务中,由于扫描设备视角受限,扫描的杏鲍菇点云出现残缺问题,基于AdaPoinTr(Adaptive geometry-aware point transformers)提出了改进的SwinPoinTr模型,实现了对残缺杏鲍菇点云的准确补全和杏鲍菇表型参数的测量。该方法在使用提出的特征重塑模块的基础上,构建具有几何感知能力的层次化Transformer编码模块,提高了模型对输入点云的利用率和模型捕捉点云细节特征的能力。然后基于泊松重建方法完成了补全点云表面重建,并测量到杏鲍菇表型参数。实验结果表明,本文所提算法在残缺杏鲍菇点云补全任务中,模型倒角距离为1.316×10^(-4),地球移动距离为21.3282,F1分数为87.87%。在表型参数估测任务中,模型对杏鲍菇菌高、体积、表面积估测结果的决定系数分别为0.9582、0.9596、0.9605,均方根误差分别为4.4213 mm、10.8185 cm^(3)、7.5778 cm^(2)。结果证实了该研究方法可以有效地补全残缺的杏鲍菇点云,可以为菇房内杏鲍菇表型参数测量提供基础。展开更多
In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technolog...In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.展开更多
输电线路的关键部位包括塔身、导线、绝缘子、避雷线以及引流线,无人机精细化导航的首要任务是构造输电线路的点云地图并从中分割出上述部位。为解决现有算法在输电线路的绝缘子、引流线等精细结构分割时精度低的问题,通过改进PointNet+...输电线路的关键部位包括塔身、导线、绝缘子、避雷线以及引流线,无人机精细化导航的首要任务是构造输电线路的点云地图并从中分割出上述部位。为解决现有算法在输电线路的绝缘子、引流线等精细结构分割时精度低的问题,通过改进PointNet++算法,提出了一种面向输电线路精细结构的点云分割方法。首先,基于无人机机载激光雷达在现场采集的点云数据,构造了输电线路点云分割数据集;其次,通过对比实验,筛选出在本输电线路场景下合理的数据增强方法,并对数据集进行了数据增强;最后,将自注意力机制以及倒置残差结构和PointNet++相结合,设计了输电线路关键部位点云语义分割算法。实验结果表明:该改进PointNet++算法在全场景输电线路现场点云数据作为输入的前提下,首次实现了对引流线、绝缘子等输电线路中精细结构和导线、杆塔塔身以及输电线路无关背景点的同时分割,平均交并比(mean intersection over union,mIoU)达80.79%,所有类别分割的平均F_(1)值(F1 score)达88.99%。展开更多
An automatic three-dimensional(3D) reconstruction method based on four-view stereo vision using checkerboard pattern is presented. Mismatches easily exist in traditional binocular stereo matching due to the repeatable...An automatic three-dimensional(3D) reconstruction method based on four-view stereo vision using checkerboard pattern is presented. Mismatches easily exist in traditional binocular stereo matching due to the repeatable or similar features of binocular images. In order to reduce the probability of mismatching and improve the measure precision, a four-camera measurement system which can add extra matching constraints and offer multiple measurements is applied in this work. Moreover, a series of different checkerboard patterns are projected onto the object to obtain dense feature points and remove mismatched points. Finally, the 3D model is generated by performing Delaunay triangulation and texture mapping on the point cloud obtained by four-view matching. This method was tested on the 3D reconstruction of a terracotta soldier sculpture and the Buddhas in the Mogao Grottoes. Their point clouds without mismatched points were obtained and less processing time was consumed in most cases relative to binocular matching. These good reconstructed models show the effectiveness of the method.展开更多
The missile-borne LiDAR has an essential prospect in precise guidance.However,the instability of the missile has a significant impact on the precision of LIDAR point cloud,thus modeling and analyzing the influencing f...The missile-borne LiDAR has an essential prospect in precise guidance.However,the instability of the missile has a significant impact on the precision of LIDAR point cloud,thus modeling and analyzing the influencing factors of point cloud generation is necessary.In this investigation,the authors modeled the point cloud calculation process of missile-borne linear array scanning LiDAR under ideal conditions and analyzed the effect of position change on the fleld of view(FOV) in the course of platform motion.Subsequently,the authors summarized that the stability concerning the field of view and the target tracking are dependent on three limiting conditions.Finally,the authors derived an error formula and analyzed several typical errors in missile platform flight,including scanner error,POS error,system integration error,and platform vibration error.Based on theo retical analysis and simulation experiments,the model proposed in this papier can provide a theoretical basis for the design of the missile-borne LiDAR system and the selection of related instruments in practice.展开更多
Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D informa...Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D information,3D information performs better in separating objects and background.However,an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes,flight heights and atmospheric disturbances.A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed,which is composed of offline phase and online phase.The performance of four global feature descriptors is compared.Considering the summed volume region(SVR) discrepancy in real objects,SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target.Highly reliable simulated data are obtained under various sensor’s altitudes,detection distances and atmospheric disturbances.The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%.展开更多
文摘In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.
文摘输电线路的关键部位包括塔身、导线、绝缘子、避雷线以及引流线,无人机精细化导航的首要任务是构造输电线路的点云地图并从中分割出上述部位。为解决现有算法在输电线路的绝缘子、引流线等精细结构分割时精度低的问题,通过改进PointNet++算法,提出了一种面向输电线路精细结构的点云分割方法。首先,基于无人机机载激光雷达在现场采集的点云数据,构造了输电线路点云分割数据集;其次,通过对比实验,筛选出在本输电线路场景下合理的数据增强方法,并对数据集进行了数据增强;最后,将自注意力机制以及倒置残差结构和PointNet++相结合,设计了输电线路关键部位点云语义分割算法。实验结果表明:该改进PointNet++算法在全场景输电线路现场点云数据作为输入的前提下,首次实现了对引流线、绝缘子等输电线路中精细结构和导线、杆塔塔身以及输电线路无关背景点的同时分割,平均交并比(mean intersection over union,mIoU)达80.79%,所有类别分割的平均F_(1)值(F1 score)达88.99%。
基金Project(2012CB725301)supported by the National Basic Research Program of ChinaProject(201412015)supported by the National Special Fund for Surveying and Mapping Geographic Information Scientific Research in the Public Welfare of ChinaProject(212000168)supported by the Basic Survey-Mapping Program of National Administration of Surveying,Mapping and Geoinformation of China
文摘An automatic three-dimensional(3D) reconstruction method based on four-view stereo vision using checkerboard pattern is presented. Mismatches easily exist in traditional binocular stereo matching due to the repeatable or similar features of binocular images. In order to reduce the probability of mismatching and improve the measure precision, a four-camera measurement system which can add extra matching constraints and offer multiple measurements is applied in this work. Moreover, a series of different checkerboard patterns are projected onto the object to obtain dense feature points and remove mismatched points. Finally, the 3D model is generated by performing Delaunay triangulation and texture mapping on the point cloud obtained by four-view matching. This method was tested on the 3D reconstruction of a terracotta soldier sculpture and the Buddhas in the Mogao Grottoes. Their point clouds without mismatched points were obtained and less processing time was consumed in most cases relative to binocular matching. These good reconstructed models show the effectiveness of the method.
基金financially supported by the China National Natural Science Foundation(Grant No.61871389)the Research Plan Project of the National University of Defense Technology(Grant No.ZK18-01-02)the Director Foundation of State Key Laboratory of Pulsed Power Laser Technology(Grant No.SKL2018ZR09)。
文摘The missile-borne LiDAR has an essential prospect in precise guidance.However,the instability of the missile has a significant impact on the precision of LIDAR point cloud,thus modeling and analyzing the influencing factors of point cloud generation is necessary.In this investigation,the authors modeled the point cloud calculation process of missile-borne linear array scanning LiDAR under ideal conditions and analyzed the effect of position change on the fleld of view(FOV) in the course of platform motion.Subsequently,the authors summarized that the stability concerning the field of view and the target tracking are dependent on three limiting conditions.Finally,the authors derived an error formula and analyzed several typical errors in missile platform flight,including scanner error,POS error,system integration error,and platform vibration error.Based on theo retical analysis and simulation experiments,the model proposed in this papier can provide a theoretical basis for the design of the missile-borne LiDAR system and the selection of related instruments in practice.
基金This research was supported by National Natural Science Foundation of China(No.61271353,61871389)Major Funding Projects of National University of Defense Technology(No.ZK18-01-02)Foundation of State Key Laboratory of Pulsed Power Laser Technology(No.SKL2018ZR09).
文摘Airborne LIDAR can flexibly obtain point cloud data with three-dimensional structural information,which can improve its effectiveness of automatic target recognition in the complex environment.Compared with 2D information,3D information performs better in separating objects and background.However,an aircraft platform can have a negative influence on LIDAR obtained data because of various flight attitudes,flight heights and atmospheric disturbances.A structure of global feature based 3D automatic target recognition method for airborne LIDAR is proposed,which is composed of offline phase and online phase.The performance of four global feature descriptors is compared.Considering the summed volume region(SVR) discrepancy in real objects,SVR selection is added into the pre-processing operations to eliminate mismatching clusters compared with the interested target.Highly reliable simulated data are obtained under various sensor’s altitudes,detection distances and atmospheric disturbances.The final experiments results show that the added step increases the recognition rate by above 2.4% and decreases the execution time by about 33%.
