While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributio...While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output.To address this problem,this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering.The key idea is to incorporate a repulsion term with a data term in energy minimization.The repulsion term is responsible for the point distribution,while the data term aims to approximate the noisy surfaces while preserving geometric features.This method is capable of handling models with fine-scale features and sharp features.Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.展开更多
Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildi...Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely;Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.展开更多
在“三跨”输电线路张力放线施工中,一旦发生事故被牵导线可能跌落冲击下方跨越网,威胁被跨越物的安全稳定运行,因此提出一种基于激光点云与建筑信息模型(building information modeling,BIM)技术的“三跨”施工跨越网动力学响应分析方...在“三跨”输电线路张力放线施工中,一旦发生事故被牵导线可能跌落冲击下方跨越网,威胁被跨越物的安全稳定运行,因此提出一种基于激光点云与建筑信息模型(building information modeling,BIM)技术的“三跨”施工跨越网动力学响应分析方法。首先利用机载激光雷达采集“三跨”施工现场的三维点云数据,使用改进的布料模拟滤波算法分割得到跨越地形点云数据,使用基于点云空间维度特征与K-Means算法实现对跨越档两侧杆塔点云数据的高精度提取;其次根据提取的点云数据结合BIM技术对目标设备及施工环境进行逆向建模,通过不同地表物体的组装堆砌,并在其上搭建施工跨越网模型;最后通过模拟事故发生时导线对跨越网的冲击碰撞,探测跨越网的承载性能及其与被跨越物之间的动态净空距离。结果表明,该方法能够提前在实际施工环境中对跨越网的动力学性能进行分析,为输电线路跨越施工提供可靠的安全保障及数据支撑,具有一定的工程价值。展开更多
针对电力线点云提取过程中自动化程度低且结果易受参数影响出现欠分割或过分割的问题,结合机载激光雷达(light detection and ranging,LiDAR)点云数据的分布特点,提出一种基于改进空间密度聚类算法的激光点云电力线的提取方法。该方法...针对电力线点云提取过程中自动化程度低且结果易受参数影响出现欠分割或过分割的问题,结合机载激光雷达(light detection and ranging,LiDAR)点云数据的分布特点,提出一种基于改进空间密度聚类算法的激光点云电力线的提取方法。该方法首先通过空间分割改进高程滤波算法完成电力线点云的粗提取;其次,利用基于距离-密度的方法和数学期望计算方法获得空间密度聚类的最佳参数,避免了繁杂的人工调参过程。实验结果显示,相较于空间密度聚类算法,所提算法效率显著提高,降低了约60%电力线提取时间,实现了单根电力线点云的自动化和高效提取。展开更多
文摘While a popular representation of 3D data,point clouds may contain noise and need filtering before use.Existing point cloud filtering methods either cannot preserve sharp features or result in uneven point distributions in the filtered output.To address this problem,this paper introduces a point cloud filtering method that considers both point distribution and feature preservation during filtering.The key idea is to incorporate a repulsion term with a data term in energy minimization.The repulsion term is responsible for the point distribution,while the data term aims to approximate the noisy surfaces while preserving geometric features.This method is capable of handling models with fine-scale features and sharp features.Extensive experiments show that our method quickly yields good results with relatively uniform point distribution.
文摘Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely;Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and the focal analysis mean filter respectively. On the other hand, the DTM slope-based filter has kept the minimum elevation of the original data, that could be due to noise in the LiDAR data unchanged. Alternatively, the three approaches have produced DTMs of decreasing maximum elevation values and consequently decreasing ranges of elevations due to increases in the filter window sizes. Moreover, the standard deviations of the created DTMs from the three filters have decreased with increasing the filter window sizes however, the decreases have been continuous and steady in the cases of the Gaussian low pass filter and the focal analysis mean filters while in the case of the DTM slope-based filter the standard deviations of the created DTMs have decreased with high rates till window size of 31 × 31 then they have kept unchanged due to more increases in the filter window sizes.
文摘在“三跨”输电线路张力放线施工中,一旦发生事故被牵导线可能跌落冲击下方跨越网,威胁被跨越物的安全稳定运行,因此提出一种基于激光点云与建筑信息模型(building information modeling,BIM)技术的“三跨”施工跨越网动力学响应分析方法。首先利用机载激光雷达采集“三跨”施工现场的三维点云数据,使用改进的布料模拟滤波算法分割得到跨越地形点云数据,使用基于点云空间维度特征与K-Means算法实现对跨越档两侧杆塔点云数据的高精度提取;其次根据提取的点云数据结合BIM技术对目标设备及施工环境进行逆向建模,通过不同地表物体的组装堆砌,并在其上搭建施工跨越网模型;最后通过模拟事故发生时导线对跨越网的冲击碰撞,探测跨越网的承载性能及其与被跨越物之间的动态净空距离。结果表明,该方法能够提前在实际施工环境中对跨越网的动力学性能进行分析,为输电线路跨越施工提供可靠的安全保障及数据支撑,具有一定的工程价值。
文摘针对电力线点云提取过程中自动化程度低且结果易受参数影响出现欠分割或过分割的问题,结合机载激光雷达(light detection and ranging,LiDAR)点云数据的分布特点,提出一种基于改进空间密度聚类算法的激光点云电力线的提取方法。该方法首先通过空间分割改进高程滤波算法完成电力线点云的粗提取;其次,利用基于距离-密度的方法和数学期望计算方法获得空间密度聚类的最佳参数,避免了繁杂的人工调参过程。实验结果显示,相较于空间密度聚类算法,所提算法效率显著提高,降低了约60%电力线提取时间,实现了单根电力线点云的自动化和高效提取。
