With the rapid development of 3D digital photography and 3D digital scanning devices, massive amount of point samples can be generated in acquisition of complex, real-world objects, and thus create an urgent need for ...With the rapid development of 3D digital photography and 3D digital scanning devices, massive amount of point samples can be generated in acquisition of complex, real-world objects, and thus create an urgent need for advanced point-based processing and editing. In this paper, we present an interactive method for blending point-based geometries by dragging-and- dropping one point-based model onto another model’s surface metaphor. We first calculate a blending region based on the polygon of interest when the user drags-and-drops the model. Radial basis function is used to construct an implicit surface which smoothly interpolates with the transition regions. Continuing the drag-and-drop operation will make the system recalculate the blending regions and reconstruct the transition regions. The drag-and-drop operation can be compound in a constructive solid geometry (CSG) manner to interactively construct a complex point-based model from multiple simple ones. Experimental results showed that our method generates good quality transition regions between two raw point clouds and can effectively reduce the rate of overlapping during the blending.展开更多
【目的】巷道点云数据的噪声去除与三维重建是实现巷道数字化建模与分析的关键环节,但目前传统单一滤波算法难以有效去除巷道点云不同尺度噪声,现有三维重建算法存在建模精度低、易失真等问题,因此需要研究获取高质量的巷道点云数据方...【目的】巷道点云数据的噪声去除与三维重建是实现巷道数字化建模与分析的关键环节,但目前传统单一滤波算法难以有效去除巷道点云不同尺度噪声,现有三维重建算法存在建模精度低、易失真等问题,因此需要研究获取高质量的巷道点云数据方法和构建高精确巷道三维模型技术。【方法】通过基于邻域半径R、最小邻域点数Imin、空间阈值σc、特征保持因子σs等参数自适应的分类巷道点云去噪算法,设计基于符号距离函数(signed distance functions,SDF)的深度学习隐式曲面重建方法。集成均值法、改进的基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法和改进的双边滤波算法,构建分类处理技术框架,集成算法自动分析巷道点云数据中的噪声类型,并通过自适应机制高效去除不同尺度噪声,实现主体点云数据的深度净化。采用PointNet++提取巷道点云局部区域特征,导入神经隐式网络学习局部上下文信息,生成全局模型SDF,并结合移动立方体算法构建精细化的巷道三维模型。【结果和结论】以安徽省张集煤矿1∶1模拟巷道为实验场景,开展多维空间的巷道点云去噪与三维重建研究。研究结果表明:(1)集成算法可根据巷道场景与噪声类别动态调整去噪策略,具备自适应优化性能,产生的Ⅰ类和Ⅱ类误差分别为1.54%和5.37%,可在保留主体点云特征的同时有效去除大、小尺度及重复点三类噪声。(2)重建算法能在保持巷道模型精度与光滑度的同时有效减少孔洞,且精准刻画复杂位置的结构细节,重建巷道的平均偏差、标准偏差、均方根误差分别为0.037、0.040、0.041 m,满足智能化矿山建设高精度要求,为矿山数字化转型升级与智能精准开采提供高质量的三维数据支撑。展开更多
【目的】解决钢箱系杆拱桥的钢拱肋在施工过程中精度控制难度大和耗时长的问题。【方法】以某钢箱系杆拱桥为工程背景,采用建筑信息模型(building information modeling,BIM)及3D激光扫描技术,对拱肋钢构件在加工制作与拼接过程中的质...【目的】解决钢箱系杆拱桥的钢拱肋在施工过程中精度控制难度大和耗时长的问题。【方法】以某钢箱系杆拱桥为工程背景,采用建筑信息模型(building information modeling,BIM)及3D激光扫描技术,对拱肋钢构件在加工制作与拼接过程中的质量检测进行信息化管控。【结果】BIM技术结合3D激光扫描技术可快速地检测钢拱肋构件的质量并监测拱肋施工线形;钢箱拱肋构件的最大制作误差在1.2 mm以内,构件在拼接过程中的最大误差在1.1 mm以内,以上误差均满足设计规范的要求;与传统检测方法相比,点云数据在各坐标轴方向的偏差为1.0~3.0 mm,平均偏差为1.2~1.5 mm,具有较高的可靠性。【结论】基于BIM+3D激光扫描技术,可实现钢箱拱肋构件施工过程中拱肋线形质量的动态管控。展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 60473106 and 60333010)the Program for Chang-jiang Scholars and Innovative Research Team in University (No. IRT0652), China
文摘With the rapid development of 3D digital photography and 3D digital scanning devices, massive amount of point samples can be generated in acquisition of complex, real-world objects, and thus create an urgent need for advanced point-based processing and editing. In this paper, we present an interactive method for blending point-based geometries by dragging-and- dropping one point-based model onto another model’s surface metaphor. We first calculate a blending region based on the polygon of interest when the user drags-and-drops the model. Radial basis function is used to construct an implicit surface which smoothly interpolates with the transition regions. Continuing the drag-and-drop operation will make the system recalculate the blending regions and reconstruct the transition regions. The drag-and-drop operation can be compound in a constructive solid geometry (CSG) manner to interactively construct a complex point-based model from multiple simple ones. Experimental results showed that our method generates good quality transition regions between two raw point clouds and can effectively reduce the rate of overlapping during the blending.
文摘【目的】巷道点云数据的噪声去除与三维重建是实现巷道数字化建模与分析的关键环节,但目前传统单一滤波算法难以有效去除巷道点云不同尺度噪声,现有三维重建算法存在建模精度低、易失真等问题,因此需要研究获取高质量的巷道点云数据方法和构建高精确巷道三维模型技术。【方法】通过基于邻域半径R、最小邻域点数Imin、空间阈值σc、特征保持因子σs等参数自适应的分类巷道点云去噪算法,设计基于符号距离函数(signed distance functions,SDF)的深度学习隐式曲面重建方法。集成均值法、改进的基于密度的聚类(density-based spatial clustering of applications with noise,DBSCAN)算法和改进的双边滤波算法,构建分类处理技术框架,集成算法自动分析巷道点云数据中的噪声类型,并通过自适应机制高效去除不同尺度噪声,实现主体点云数据的深度净化。采用PointNet++提取巷道点云局部区域特征,导入神经隐式网络学习局部上下文信息,生成全局模型SDF,并结合移动立方体算法构建精细化的巷道三维模型。【结果和结论】以安徽省张集煤矿1∶1模拟巷道为实验场景,开展多维空间的巷道点云去噪与三维重建研究。研究结果表明:(1)集成算法可根据巷道场景与噪声类别动态调整去噪策略,具备自适应优化性能,产生的Ⅰ类和Ⅱ类误差分别为1.54%和5.37%,可在保留主体点云特征的同时有效去除大、小尺度及重复点三类噪声。(2)重建算法能在保持巷道模型精度与光滑度的同时有效减少孔洞,且精准刻画复杂位置的结构细节,重建巷道的平均偏差、标准偏差、均方根误差分别为0.037、0.040、0.041 m,满足智能化矿山建设高精度要求,为矿山数字化转型升级与智能精准开采提供高质量的三维数据支撑。
