<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[点云侠的博客]]></title><description><![CDATA[个人笔记]]></description><link>https://blog.youkuaiyun.com/qq_36686437</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; qq_36686437]]></copyright><item><title><![CDATA[单调分段三次插值]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/168962061</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/168962061</guid><author>qq_36686437</author><pubDate>Sat, 24 Oct 2026 10:51:58 +0800</pubDate><description><![CDATA[推导了三次函数在区间上单调的充分必要条件。利用这些条件，我们开发了一种算法，用于构造对单调数据的视觉上令人满意的单调分段三次插值函数。文中给出了几个示例，将该算法与其他插值方法进行了比较。]]></description><category></category></item><item><title><![CDATA[python 根据后缀对点云文件分组]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/165858969</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/165858969</guid><author>qq_36686437</author><pubDate>Fri, 18 Sep 2026 11:25:16 +0800</pubDate><description><![CDATA[本文介绍了一个基于Open3D与Python标准库的PCD文件自动分类处理方案。代码可自动扫描指定目录，识别以_raw和_filtered结尾的点云文件，并分别归类至raw_data和filtered_data文件夹，同时自动创建缺失目录。通过Open3D读写点云数据，确保文件完整性，并支持弹窗提示结果。适用于批量处理点云数据的场景。]]></description><category></category></item><item><title><![CDATA[python 实现点云投影与边界拟合可视化]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/165835175</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/165835175</guid><author>qq_36686437</author><pubDate>Fri, 18 Sep 2026 08:46:37 +0800</pubDate><description><![CDATA[在三维点云处理中，我们经常需要把点云投影到二维平面，以便观察隧道断面、道路边界或物体轮廓。本文剖析一段基于 Python、Open3D 和 Matplotlib 的可视化脚本，帮助读者理解如何将 PCD 点云文件绘制成 2x2 复合图，并叠加边界点与拟合边界线。]]></description><category></category></item><item><title><![CDATA[PCL 计算距离最大的两个点并保存]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/165104874</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/165104874</guid><author>qq_36686437</author><pubDate>Sun, 13 Sep 2026 23:39:44 +0800</pubDate><description><![CDATA[本文介绍了一种基于暴力枚举的三维点云最大距离求解方法]]></description><category></category></item><item><title><![CDATA[PCL 计算中位数]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/160223597</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/160223597</guid><author>qq_36686437</author><pubDate>Tue, 01 Sep 2026 09:05:48 +0800</pubDate><description><![CDATA[使用PCL内置函数计算中位数]]></description><category></category></item><item><title><![CDATA[基于LO-RANSAC算法拟合圆柱]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162539359</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162539359</guid><author>qq_36686437</author><pubDate>Tue, 01 Sep 2026 09:02:38 +0800</pubDate><description><![CDATA[结合RANSAC随机采样拟合圆柱与LM最小二乘法局部优化，有效去除异常值，提升杆状地物（路灯、指示牌等）提取的精度与鲁棒性。]]></description><category></category></item><item><title><![CDATA[Ceres——非线性最小二乘拟合曲线]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162311201</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162311201</guid><author>qq_36686437</author><pubDate>Wed, 08 Jul 2026 07:56:01 +0800</pubDate><description><![CDATA[利用Ceres求解器进行非线性曲线拟合的方法。]]></description><category></category></item><item><title><![CDATA[【论文阅读】——基于地面激光扫描的盾构隧道变形分析]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162332252</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162332252</guid><author>qq_36686437</author><pubDate>Wed, 08 Jul 2026 07:55:39 +0800</pubDate><description><![CDATA[论文阅读]]></description><category></category></item><item><title><![CDATA[PCL Laplace算法实现点云去噪]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162575630</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162575630</guid><author>qq_36686437</author><pubDate>Mon, 06 Jul 2026 09:38:27 +0800</pubDate><description><![CDATA[基于地面三维激光的溶洞点云数据处理及应用研究_徐光禹
]]></description><category></category></item><item><title><![CDATA[PCL 基于改进半径滤波的远噪声点去噪]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162524627</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162524627</guid><author>qq_36686437</author><pubDate>Mon, 06 Jul 2026 09:37:58 +0800</pubDate><description><![CDATA[结合改进半径滤波和局部平面拟合的点云去噪算法]]></description><category></category></item><item><title><![CDATA[PCL 基于凸包顶点的圆柱拟合]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162412065</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162412065</guid><author>qq_36686437</author><pubDate>Fri, 03 Jul 2026 08:31:24 +0800</pubDate><description><![CDATA[[1]赵昭明,何华,李郭超,等. 基于凸包算法的隧道点云中轴线提取 [J]. 科学技术创新, 2024, (19): 37-40.]]></description><category></category></item><item><title><![CDATA[PCL 二维Gamma 滤波算法]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162523969</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162523969</guid><author>qq_36686437</author><pubDate>Fri, 03 Jul 2026 08:30:39 +0800</pubDate><description><![CDATA[[1]刘德儿,李瑞雪,杨鹏. 基于二维Gamma分布的激光点云去噪 [J]. 激光与红外, 2019, 49 (10): 1172-1178.]]></description><category></category></item><item><title><![CDATA[PCL 弦差法实现点云精简]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162382313</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162382313</guid><author>qq_36686437</author><pubDate>Mon, 29 Jun 2026 08:29:21 +0800</pubDate><description><![CDATA[[1]周正鋆. 隧道点云数据的配准方法研究[D]. 东华理工大学, 2014.]]></description><category></category></item><item><title><![CDATA[PCL 垂距法实现点云精简]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162383066</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162383066</guid><author>qq_36686437</author><pubDate>Mon, 29 Jun 2026 08:29:06 +0800</pubDate><description><![CDATA[垂距法实现点云精简]]></description><category></category></item><item><title><![CDATA[Ceres——非线性最小二乘拟合二维圆]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162280232</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162280232</guid><author>qq_36686437</author><pubDate>Thu, 25 Jun 2026 09:49:54 +0800</pubDate><description><![CDATA[基于非线性最小二乘的圆拟合方法。首先建立圆的一般方程模型，针对k个观测点构建最小二乘问题。比较了两种代价函数方案：方案A采用几何距离但非线性强，方案B（Ceres官方推荐）通过消除根号使优化更稳定。文中详细给出了C++实现代码，包括数据生成（含高斯噪声和异常点）、残差计算模型构建、鲁棒核函数配置以及优化求解过程。实验结果表明，该方法能有效拟合圆心坐标和半径，输出详细的优化过程报告，验证了算法的有效性。代码采用AutoDiff自动求导，并引入Huber核函数增强对异常值的鲁棒性。]]></description><category></category></item><item><title><![CDATA[OpenCV——均值滤波]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162294744</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162294744</guid><author>qq_36686437</author><pubDate>Thu, 25 Jun 2026 09:45:09 +0800</pubDate><description><![CDATA[本文介绍了均值滤波的原理与实现方法。均值滤波是一种线性滤波算法，通过计算目标像素周围邻域像素的平均值来替代原像素值，可有效平滑图像噪声。文章详细阐述了其数学公式（g(x,y)=1/m∑f(x,y)）和3×3、9×9等常用卷积核尺寸，分析了算法优缺点：计算简单高效但对边缘保护不足，且核尺寸越大图像越模糊。提供了C++和Python两种实现代码，均基于OpenCV的blur()函数，并展示了不同卷积核尺寸的滤波效果对比图。结果显示，9×9核的降噪效果更明显但图像更模糊，验证了理论分析。]]></description><category></category></item><item><title><![CDATA[基于直接点云间距统计采样的三维点云形变追踪方法]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162245234</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162245234</guid><author>qq_36686437</author><pubDate>Wed, 24 Jun 2026 18:44:29 +0800</pubDate><description><![CDATA[依托激光扫描仪或多视图摄影测量技术生成的基础设施高密度三维（3D）点云，具备作为无损检测信息数据源的巨大潜力。]]></description><category></category></item><item><title><![CDATA[PCL 基于高程改进的体素滤波]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162278926</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162278926</guid><author>qq_36686437</author><pubDate>Wed, 24 Jun 2026 18:43:26 +0800</pubDate><description><![CDATA[本文提出一种面向高程极值保留的改进体素滤波算法，旨在解决传统体素滤波因取重心或随机采样导致的高程特征丢失问题。该算法通过在每个三维体素网格内仅保留高程（Z轴）最大的点，实现点云数据的稀疏化，同时最大程度维持地物顶部形态（如建筑物屋顶、树冠等）。]]></description><category></category></item><item><title><![CDATA[Open3D (C++) 三维轨迹点的等间距三次样条平滑与插值（间距可控）]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/148699412</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/148699412</guid><author>qq_36686437</author><pubDate>Tue, 23 Jun 2026 08:47:17 +0800</pubDate><description><![CDATA[1]]></description><category></category></item><item><title><![CDATA[基于全站仪的隧道位移测量精度分析]]></title><link>https://blog.youkuaiyun.com/qq_36686437/article/details/162210889</link><guid>https://blog.youkuaiyun.com/qq_36686437/article/details/162210889</guid><author>qq_36686437</author><pubDate>Tue, 23 Jun 2026 08:46:43 +0800</pubDate><description><![CDATA[研究了全站仪远程测距法（RDM）在隧道位移监测中的应用，推导了拱顶下沉与围岩收敛的计算公式及误差传播模型。结果表明，当采用测距精度为2 mm+2 ppm、测角精度1″的全站仪，且后视基准点与监测断面距离为50-150 m、全站仪设站距离40-60 m时，该方法位移测量精度可达1 mm。研究为提升隧道施工监测效率与精度提供了理论依据，验证了RDM法在简化操作流程、降低成本的同时，仍能保证高精度监测的优势。 ]]></description><category></category></item></channel></rss>