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pcl是根据强度值进行双边滤波，这里用法线计算。 双边滤波具有一定的保留边缘特征的功能，本质上是一种平滑算法（滤波前后点云数量不变），算法原理： 具体参考《The Bilateral Filter for Point Clouds》 作者：Julie Digne, Carlo de Franchis  代码：
#include
#include
#include
#include
#include
#include
#include
using point = pcl::PointXYZ;
using normal = pcl::Normal;
using cloud = pcl::PointCloud;
using cloudNormal = pcl::PointCloud;
int main()
{
//三个参数
const double dist_weigh =0.04;
const double normal_weigh = 0.02;
const int k = 40;
cloud::Ptr cloud_in(new cloud), cloud_out(new cloud);
std::string file_name("noisedchair.pcd");
pcl::io::loadPCDFile(file_name, *cloud_in);
//kdtree
pcl::search::KdTree::Ptr tree(new pcl::search::KdTree);
tree->setInputCloud(cloud_in);
//计算法线
pcl::NormalEstimation ne;
cloudNormal cloud_normals;
ne.setInputCloud(cloud_in);
ne.setSearchMethod(tree);
ne.setKSearch(k);
ne.compute(cloud_normals);
for (size_t i = 0; i size(); ++i)
{
std::vector indices;
std::vectordist_square;
indices.reserve(k);
indices.reserve(k);
//
double zeta = 0.0;
double sum = 0.0;
if (tree->nearestKSearch(cloud_in->points[i], k, indices, dist_square) > 0)
{
for (size_t j = 1; j
zeta = 0.0;
}
else
{
zeta /= sum;
}
point smoothed_point;
smoothed_point.x = cloud_in->points[i].x + cloud_normals.at(i).normal_x * zeta;
smoothed_point.y = cloud_in->points[i].y + cloud_normals.at(i).normal_y * zeta;
smoothed_point.z = cloud_in->points[i].z + cloud_normals.at(i).normal_z * zeta;
cloud_out->push_back(smoothed_point);
}
cloud_out->width = cloud_out->size();
cloud_out->height = 1;
cloud_out->resize(cloud_out->width * cloud_out->height);
pcl::io::savePCDFile("smoothed" + file_name, *cloud_out);
std::cout |