共查询到18条相似文献,搜索用时 49 毫秒
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本文首先简要分析了现有点云简化算法的优缺点,接着设计了一种基于kd_tree数据索引与曲率采样结合的高效简化策略,充分利用曲率采样的精度优势与kd_tree索引的速度优势,实现了基于kd_tree索引的曲率自适应点云简化算法。试验表明,该算法在减少点云数据量的同时,能够较好地保证模型中的特征点,在速度与效果上都达到了较为理想的结果。 相似文献
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针对传统的点云简化算法导致特征区域容易丢失的问题,提出了一种新的基于特征约束的点云简化的算法。首先对散乱点云用KD—TREE建立起空间拓扑关系,在此基础上建立起单个点的K-邻域。然后对K-邻域内建立起最小二乘平面,设定合理的阈值来度量数据点的重要性。依据特征点的分布估算每个点的简化距离阈值,以此为基础对每个点进行自适应简化。实验证明该算法能满足在点云数据简化过程中检测并保留特征点的要求。 相似文献
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基于区域生长的LIDAR点云数据滤波 总被引:3,自引:0,他引:3
将区域生长算法引入LIDAR点云滤波,其原理与图像处理中的区域生长原理相似。首先选择可靠的种子点,当待定点与种子点的高差满足所设阈值时,则待定点为地面点,否则作为地物点滤去。当不再有激光点满足条件时,生长结束。该算法无需对原始数据进行插值,无需迭代,因此滤波速度快。与传统的最大局部倾斜度过滤算法(MLS)和扩大窗口高程阈值滤波方法相比,该算法的实验结果更优。 相似文献
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基于深度学习方法,借鉴二维图像卷积的思想,设计了一种适合三维点云的卷积操作。点云卷积的作用域是局部球形邻域,输入为三维坐标和空间几何关系。通过点云卷积提取局部特征,使用最远点采样算法采集邻域中心点,根据半径构建球形局部邻域,使用多层感知器(multi-layer perceptron,MLP)网络学习空间关系权重,将学习到的关系权重和输入特征相乘,实现卷积操作。基于三维点云卷积,构建了一个多层分类网络模型实现点云分类。使用道路场景的黄石路数据集进行分类实验,结果证明了所提方法的有效性。 相似文献
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建筑物内部结构和环境复杂,由此产生的噪声等影响使其无法直接运用于室外建模中已经成熟的点云分割算法。为降低噪声带来的影响,本文提出利用直方图统计法,分别对点云进行Z轴方向和X-Y轴方向上的直方图统计,从而分割出地板面,天花板面以及墙面的"候选点"。以K-D树构建空间数据索引,计算点云中各点的法向量以及曲率,将"候选点"中面与面相交处曲率"突变"的点去除,利用区域生长算法分割出建筑物的地板面、墙面以及天花板面。以NavVis公司的M3三维激光扫描车获取的室内SLAM点云数据,对本文方法检验,实验结果表明该方法能有效地降低噪声带来的影响,并且可以对平面点云数据进行分割提取。 相似文献
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为了提高变形监测中地铁隧道断面点截取的效率,文章提出了基于kd-tree和法向量估计的局部点云简化方法,对BaySAC算法的三维激光点云二次参数曲面拟合方法进行改进:利用kd-tree建立点云数据的空间拓扑关系,计算出每个数据点的k邻域;然后使用平面拟合方法获取法矢量;最后根据点云数据法矢量变化程度,采用法矢量自适应得到压缩后的点云数据。实验证明该方法既能较大程度地简化点云,简化结果比较均匀,又具有不破坏细小特征的特点,进一步改进了BaySAC算法的二次参数曲面拟合方法。 相似文献
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机载LiDAR点云航带平差方法研究 总被引:1,自引:0,他引:1
以航带平差作为系统误差消除的关键技术,提出了基于无控制三维表面匹配的方法,并用最小高程差(LZD)和最小法向距离(LND)两种算法加以实现。实验表明,LND和LZD算法的平差结果均可满足工程精度的需求;LZD较LND算法的整体计算效率偏低,但其精度较高;与商业软件TMatch的结果相比,LZD的精度和其相当,且两种方式在TMatch软件平差失败时也能成功地完成航带平差任务。 相似文献
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针对地面激光扫描仪获取的点云数据具有数据量大、冗余数据多、分布不均匀的特点,提出了一种基于扇形划分的地面激光扫描点云数据化简方法.首先根据设定的初始扇形块半径和弧长计算整个点云范围内的扇形块参数;然后再将点云按坐标分配到相应的扇形块中;最后对每个扇形块中的点采用相应的计算方法得到化筒后的点云.实验证明,该算法能够较好地... 相似文献
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针对机载LiDAR点云数据航带间高差较大问题,对有无构架线及POS解算精度不同两种情况进行分析。发现机载POS数据解算精度对点云数据航带间高差有较大影响,并得出减小点云数据高差的POS数据解算精度需要满足的条件。 相似文献
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Wei Xuan Xianghong Hua Xijiang Chen Jingui Zou Xiaoxing He 《Journal of the Indian Society of Remote Sensing》2018,46(4):581-589
With the development of modern 3D measurement technologies, it becomes easy to capture dense point cloud datasets. To settle the problem of pruning the redundant points and fast reconstruction, simplification for point cloud is a necessary step during the processing. In this paper, a new method is proposed to simplify point cloud data. The kernel procedure of the method is to evaluate the importance of points based on local entropy of normal angle. After the estimation of normal vectors, the importance evaluation of points is derived based on normal angles and the theory of information entropy. The simplification proceeds and finishes by removing the least important points and updating the normal vectors and importance values progressively until user-specified reduction ratio is reached. To evaluate the accuracy of the simplification results quantitatively, an indicator is determined by calculating the mean entropy of the simplified point cloud. Furthermore, the performance of the proposed approach is illustrated with two sets of validation experiments where other three classical simplification methods are employed for contrast. The results show that the proposed method performs much better than other three methods for point cloud simplification. 相似文献
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Qian-ning Zhang Ze-chun Huang Zhu Xu Hai-bin Shang 《Journal of the Indian Society of Remote Sensing》2018,46(11):1773-1784
It is difficult to obtain digital elevation model (DEM) in the mountainous regions. As an emerging technology, Light Detection and Ranging (LiDAR) is an enabling technology. However, the amount of points obtained by LiDAR is huge. When processing LiDAR point cloud, huge data will lead to a rapid decline in data processing speed, so it is necessary to thin LiDAR point cloud. In this paper, a new terrain sampling rule had been built based on the integrated terrain complexity, and then based on the rule a LiDAR point cloud simplification method, which was referred as to TCthin, had been proposed. The TCthin method was evaluated by experiments in which XUthin and Lasthin were selected as the TCthin’s comparative methods. The TCthin’s simplification degree was estimated by the simplification rate value, and the TCthin’s simplification quality was evaluated by Root Mean Square Deviation. The experimental results show that the TCthin method can thin LiDAR point cloud effectively and improve the simplification quality, and at 5 m, 10 m, 30 m scale levels, the TCthin method has a good applicability in the areas with different terrain complexity. This study has theoretical and practical value in sampling theory, thinning LiDAR point cloud, building high-precision DEM and so on. 相似文献
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