如何在保持其顺序的同时对点云进行下采样？ (PCL C++)

Question

I want to downsample incoming pointclouds by using the passthrough filter provided by the pcl library and also maintain the order of the given clouds. My current solution is as follows:

using point_type_colored = pcl::PointXYZRGB;
using point_cloud_colored = pcl::PointCloud<point_type_colored>;

point_cloud_colored::Ptr registration_util::pass_through_filter(
     const point_cloud_colored::Ptr& cloud_in, double p_t_x_min, double p_t_x_max,
     double p_t_y_min, double p_t_y_max, double p_t_z_min, double p_t_z_max)
{
     point_cloud_colored::Ptr filtered_cloud(new point_cloud_colored);
     *filtered_cloud = *cloud_in;
     pcl::PassThrough<point_type_colored> pass_filter;

     //filter x boundaries
     if (p_t_x_min != p_t_x_max)
     {
         pass_filter.setInputCloud(filtered_cloud);
         pass_filter.setFilterFieldName("x");
         pass_filter.setFilterLimits(p_t_x_min, p_t_x_max);
         pass_filter.filter(*filtered_cloud);
     }

     //filter y boundaries
     if (p_t_y_min != p_t_y_max)
     {
         pass_filter.setInputCloud(filtered_cloud);
         pass_filter.setFilterFieldName("y");
         pass_filter.setFilterLimits(p_t_y_min, p_t_y_max);
         pass_filter.filter(*filtered_cloud);
     }

     //filter z boundaries
     if (p_t_z_min != p_t_z_max)
     {
         pass_filter.setInputCloud(filtered_cloud);
         pass_filter.setFilterFieldName("z");
         pass_filter.setFilterLimits(p_t_z_min, p_t_z_max);
         pass_filter.filter(*filtered_cloud);
     }

     return std::move(filtered_cloud);
}

The input cloud has a defined width (= 1280) and height (=720) which means the point cloud is ordered. But the output cloud has only the height of one and a width of 92160 which means the cloud has lost its order due to downsampling.

How do I keep the original order of the input cloud? If it is possible are there similar solutions for other downsampling methods like random filtering?

Answer 1

You are looking for the function setKeepOrganized . Providing true will set each filtered point to NaN instead of removing the point.

You can also change the value to set it to with setUserFilterValue , but keeping it NaN has the benefit that other PCL algorithms won't consider these points for their algorithm.