pcl之MLS平滑

MLS移动最小二乘法

移动最小二乘法对点云进行平滑处理，数据重采样，并且可以计算优化的估计法线,还可以用来曲面重建

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/surface/mls.h>
#include <pcl/console/time.h>
#include <pcl/point_cloud.h>
using namespace std;
typedef pcl::PointXYZ point;
typedef pcl::PointCloud<point> pointcloud;


int main (int argc,char **argv)
{
        pointcloud::Ptr cloud (new pointcloud);
        pcl::io::loadPCDFile(argv[1],*cloud);
        cout<<"points size is:"<<cloud->size()<<endl;
        pcl::search::KdTree<point>::Ptr tree (new pcl::search::KdTree<point>);

        //创建存储的mls对象
        //    pcl::PointCloud<pcl::PointNormal> mls_points;
         pcl::PointCloud<point> mls_points;

        //创建mls对象
        //  pcl::MovingLeastSquares<point,pcl::PointNormal> mls;
        
        pcl::MovingLeastSquares<point,point> mls;
        mls.setComputeNormals(true);
        mls.setInputCloud(cloud);
        mls.setPolynomialFit(true); //设置为true则在平滑过程中采用多项式拟合来提高精度
        mls.setPolynomialOrder(2); //MLS拟合的阶数，默认是2
        mls.setSearchMethod(tree);
        mls.setSearchRadius(1.1);  //这个值越大，输出的点越多
        
        mls.process(mls_points);

        cout<<"mls poits size is: "<<mls_points.size()<<endl;
        
        // Save output
        pcl::io::savePCDFile ("mid-mls.pcd", mls_points);

}
return 0;