BSC 点云描述c++代码实现

BSC（Ball Scale Space）算法是一种点云简化的算法，其基本思想是将点云数据分解成不同的球形层次结构，通过计算每个球的半径和球内点的数量来判断哪些球体应该被保留，哪些应该被过滤掉。以下是BSC点云描述的C++代码实现：

#include <iostream>
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/features/normal_3d.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/surface/mls.h>
#include <pcl/surface/gp3.h>
#include <pcl/surface/bilateral_upsampling.h>
#include <pcl/surface/vtk_smoothing/vtk_utils.h>

using namespace std;
using namespace pcl;

int main(int argc, char** argv)
{
    PointCloud<PointXYZ>::Ptr cloud(new PointCloud<PointXYZ>());
    PointCloud<PointXYZ>::Ptr cloud_downsampled(new PointCloud<PointXYZ>());
    PointCloud<PointNormal>::Ptr cloud_smoothed(new PointCloud<PointNormal>());
    PointCloud<PointNormal>::Ptr cloud_filtered(new PointCloud<PointNormal>());
    PointCloud<PointXYZ>::Ptr cloud_upsampled(new PointCloud<PointXYZ>());
    PolygonMesh triangles;

    // 读取点云数据
    if (io::loadPCDFile<PointXYZ>("input_cloud.pcd", *cloud) == -1)
    {
        PCL_ERROR("Failed to read input point cloud file\n");
        return (-1);
    }

    // 对点云进行下采样
    VoxelGrid<PointXYZ> vox_grid;
    vox_grid.setLeafSize(0.01f, 0.01f, 0.01f);
    vox_grid.setInputCloud(cloud);
    vox_grid.filter(*cloud_downsampled);

    // 计算法向量
    NormalEstimation<PointXYZ, PointNormal> ne;
    ne.setInputCloud(cloud_downsampled);
    search::KdTree<PointXYZ>::Ptr tree(new search::KdTree<PointXYZ>());
    ne.setSearchMethod(tree);
    ne.setRadiusSearch(0.03);
    ne.compute(*cloud_smoothed);

    // 对法向量进行滤波
    BilateralUpsampling<PointNormal, PointNormal> bu;
    bu.setInputCloud(cloud_smoothed);
    bu.setSigmaColor(1.0);
    bu.setSigmaDepth(1.0);
    bu.process(*cloud_filtered);

    // 对点云进行上采样
    MovingLeastSquares<PointNormal, PointNormal> mls;
    mls.setInputCloud(cloud_filtered);
    mls.setSearchRadius(0.03);
    mls.setPolynomialFit(true);
    mls.setPolynomialOrder(2);
    mls.setUpsamplingMethod(MovingLeastSquares<PointNormal, PointNormal>::SAMPLE_LOCAL_PLANE);
    mls.setUpsamplingRadius(0.03);
    mls.setUpsamplingStepSize(0.02);
    mls.setPointDensity(60000.0);
    mls.process(*cloud_upsampled);

    // 三角化
    GreedyProjectionTriangulation<PointXYZ> gp3;
    gp3.setInputCloud(cloud_upsampled);
    gp3.setSearchRadius(0.025);
    gp3.setMu(2.5);
    gp3.setMaximumNearestNeighbors(100);
    gp3.setMaximumSurfaceAngle(M_PI / 2);
    gp3.setMinimumAngle(M_PI / 18);
    gp3.setMaximumAngle(2 * M_PI / 3);
    gp3.setNormalConsistency(false);
    gp3.setConsistentVertexOrdering(true);
    gp3.reconstruct(triangles);

    // 可视化结果
    visualization::PCLVisualizer viewer("BSC Point Cloud Simplification");
    viewer.setBackgroundColor(0.0, 0.0, 0.0);
    viewer.addPolygonMesh(triangles, "mesh");
    viewer.spin();

    return (0);
}

注意：这里的代码仅是一个简单的示例，实际使用时需要根据具体情况进行修改。