C++ 代码：使用最小生成树连接并可视化 PLY 点云文件

下面是一个简单的 C++ 代码示例，用于读取 PLY 点云文件并计算最小生成树，然后将其可视化。

请注意，本代码示例假定 PLY 文件格式为 ASCII 格式，并且使用了 PCL 库进行点云处理和可视化。

#include <pcl/io/ply_io.h>
#include <pcl/point_cloud.h>
#include <pcl/point_types.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/common/common.h>
#include <pcl/common/transforms.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/passthrough.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/features/normal_3d.h>
#include <pcl/surface/mls.h>
#include <pcl/features/moment_of_inertia_estimation.h>
#include <pcl/segmentation/sac_segmentation.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/segmentation/region_growing.h>
#include <pcl/segmentation/conditional_euclidean_clustering.h>
#include <pcl/registration/icp.h>
#include <pcl/registration/ndt.h>
#include <pcl/registration/gicp.h>
#include <pcl/registration/transforms.h>
#include <pcl/sample_consensus/ransac.h>
#include <pcl/sample_consensus/sac_model_registration.h>
#include <pcl/ModelCoefficients.h>
#include <pcl/filters/crop_box.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/search/kdtree.h>
#include <pcl/geometry/polygon_operations.h>
#include <pcl/geometry/triangle_mesh.h>

int main(int argc, char** argv)
{
    // Load PLY file
    pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
    pcl::PLYReader reader;
    reader.read('input.ply', *cloud);

    // Build KD-Tree
    pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);
    tree->setInputCloud(cloud);

    // Initialize variables for minimum spanning tree
    std::vector<bool> visited(cloud->points.size(), false);
    std::vector<int> parent(cloud->points.size(), -1);
    std::vector<float> distance(cloud->points.size(), std::numeric_limits<float>::max());

    // Compute minimum spanning tree using Prim's algorithm
    int current = 0;
    visited[current] = true;
    distance[current] = 0.0f;
    while (true)
    {
        // Update distances to neighboring points
        std::vector<int> indices;
        std::vector<float> distances;
        tree->nearestKSearch(cloud->points[current], 6, indices, distances);
        for (int i = 1; i < indices.size(); i++)
        {
            int index = indices[i];
            float dist = distances[i];
            if (!visited[index] && dist < distance[index])
            {
                parent[index] = current;
                distance[index] = dist;
            }
        }

        // Find point with smallest distance to tree
        float minDist = std::numeric_limits<float>::max();
        int next = -1;
        for (int i = 0; i < cloud->points.size(); i++)
        {
            if (!visited[i] && distance[i] < minDist)
            {
                minDist = distance[i];
                next = i;
            }
        }

        // Check if all points are visited
        if (next == -1)
            break;

        // Add point to tree
        current = next;
        visited[current] = true;
    }

    // Create line segments from tree
    pcl::PointCloud<pcl::PointXYZ>::Ptr lines(new pcl::PointCloud<pcl::PointXYZ>);
    for (int i = 0; i < parent.size(); i++)
    {
        if (parent[i] != -1)
        {
            pcl::PointXYZ p1 = cloud->points[i];
            pcl::PointXYZ p2 = cloud->points[parent[i]];
            lines->push_back(p1);
            lines->push_back(p2);
        }
    }

    // Visualize point cloud and lines
    boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer('Viewer'));
    viewer->addPointCloud(cloud, 'cloud');
    viewer->addPointCloud(lines, 'lines');
    viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 1.0, 1.0, 'lines');
    viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_LINE_WIDTH, 2.0, 'lines');
    viewer->spin();

    return 0;
}