针对有组织的点云，代码如下：

#include <pcl/PCLPointCloud2.h>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/fast_bilateral.h>
#include <pcl/console/print.h>
#include <pcl/console/parse.h>
#include <pcl/console/time.h>

using namespace std;
using namespace pcl;
using namespace pcl::io;
using namespace pcl::console;

float default_sigma_s = 5.0f;
float default_sigma_r = 0.03f;

void
printHelp (int, char **argv)
{
  print_error ("Syntax is: %s input.pcd output.pcd <options> [optional_arguments]\n", argv[0]);
  print_info ("  where options are:\n");
  print_info ("                     -sigma_s X = use a sigma S value of X (default: "); 
  print_value ("%f", default_sigma_s); print_info (")\n");
  print_info ("                     -sigma_r X = use a sigma R value of X (default: "); 
  print_value ("%f", default_sigma_r); print_info (")\n");
  print_info ("\nOptional arguments are:\n");
  print_info ("                     -input_dir X  = batch process all PCD files found in input_dir\n");
  print_info ("                     -output_dir X = save the processed files from input_dir in this directory\n");
}

bool
loadCloud (const string &filename, pcl::PCLPointCloud2 &cloud,
           Eigen::Vector4f &translation, Eigen::Quaternionf &orientation)
{
  if (loadPCDFile (filename, cloud, translation, orientation) < 0)
    return (false);

  return (true);
}

void
compute (const pcl::PCLPointCloud2::ConstPtr &input, pcl::PCLPointCloud2 &output,
         float sigma_s = 5.f, float sigma_r = 0.03f)
{
  // Convert data to PointCloud<T>
  PointCloud<PointXYZ>::Ptr xyz (new PointCloud<PointXYZ>);
  fromPCLPointCloud2 (*input, *xyz);

  TicToc tt;
  tt.tic ();

  // Apply the filter
  FastBilateralFilter<PointXYZ> fbf;
  fbf.setInputCloud (xyz);
  fbf.setSigmaS (sigma_s);
  fbf.setSigmaR (sigma_r);
  PointCloud<PointXYZ> xyz_filtered;
  fbf.filter (xyz_filtered);

  print_highlight ("Filtered data in "); print_value ("%g", tt.toc ()); print_info (" ms for "); print_value ("%lu", xyz_filtered.size ()); print_info (" points.\n");

  // Convert data back
  pcl::PCLPointCloud2 output_xyz;
  toPCLPointCloud2 (xyz_filtered, output_xyz);
  concatenateFields (*input, output_xyz, output);
}

void
saveCloud (const string &filename, const pcl::PCLPointCloud2 &output,
           const Eigen::Vector4f &translation, const Eigen::Quaternionf &orientation)
{
  PCDWriter w;
  w.writeBinaryCompressed (filename, output, translation, orientation);
}

int
batchProcess (const vector<string> &pcd_files, string &output_dir, float sigma_s, float sigma_r)
{
#if _OPENMP
#pragma omp parallel for
#endif
  for (int i = 0; i < int (pcd_files.size ()); ++i)
  {
    // Load the first file
    Eigen::Vector4f translation;
    Eigen::Quaternionf rotation;
    pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2);
    if (!loadCloud (pcd_files[i], *cloud, translation, rotation)) 
      continue;

    // Perform the feature estimation
    pcl::PCLPointCloud2 output;
    compute (cloud, output, sigma_s, sigma_r);

    // Prepare output file name
    string filename = pcd_files[i];
    boost::trim (filename);
    vector<string> st;
    boost::split (st, filename, boost::is_any_of ("/\\"), boost::token_compress_on);
    
    // Save into the second file
    stringstream ss;
    ss << output_dir << "/" << st.at (st.size () - 1);
    saveCloud (ss.str (), output, translation, rotation);
  }
  return (0);
}

/* ---[ */
int
main (int argc, char** argv)
{
  print_info ("Smooth depth data using a FastBilateralFilter. For more information, use: %s -h\n", argv[0]);

  if (argc < 3)
  {
    printHelp (argc, argv);
    return (-1);
  }

  bool batch_mode = false;

  // Command line parsing
  float sigma_s = default_sigma_s;
  float sigma_r = default_sigma_r;
  parse_argument (argc, argv, "-sigma_s", sigma_s);
  parse_argument (argc, argv, "-sigma_r", sigma_r);
  string input_dir, output_dir;
  if (parse_argument (argc, argv, "-input_dir", input_dir) != -1)
  {
    PCL_INFO ("Input directory given as %s. Batch process mode on.\n", input_dir.c_str ());
    if (parse_argument (argc, argv, "-output_dir", output_dir) == -1)
    {
      PCL_ERROR ("Need an output directory! Please use -output_dir to continue.\n");
      return (-1);
    }

    // Both input dir and output dir given, switch into batch processing mode
    batch_mode = true;
  }

  if (!batch_mode)
  {
    // Parse the command line arguments for .pcd files
    vector<int> p_file_indices;
    p_file_indices = parse_file_extension_argument (argc, argv, ".pcd");
    if (p_file_indices.size () != 2)
    {
      print_error ("Need one input PCD file and one output PCD file to continue.\n");
      return (-1);
    }

    print_info ("Smoothing data with a sigma S/R of: "); 
    print_value ("%f / %f\n", sigma_s, sigma_r);

    // Load the first file
    Eigen::Vector4f translation;
    Eigen::Quaternionf rotation;
    pcl::PCLPointCloud2::Ptr cloud (new pcl::PCLPointCloud2);
    if (!loadCloud (argv[p_file_indices[0]], *cloud, translation, rotation)) 
      return (-1);

    // Perform the feature estimation
    pcl::PCLPointCloud2 output;
    compute (cloud, output, sigma_s, sigma_r);

    // Save into the second file
    saveCloud (argv[p_file_indices[1]], output, translation, rotation);
  }
  else
  {
    if (input_dir != "" && boost::filesystem::exists (input_dir))
    {
      vector<string> pcd_files;
      boost::filesystem::directory_iterator end_itr;
      for (boost::filesystem::directory_iterator itr (input_dir); itr != end_itr; ++itr)
      {
        // Only add PCD files
        if (!is_directory (itr->status ()) && boost::algorithm::to_upper_copy (boost::filesystem::extension (itr->path ())) == ".PCD" )
        {
          pcd_files.push_back (itr->path ().string ());
          PCL_INFO ("[Batch processing mode] Added %s for processing.\n", itr->path ().string ().c_str ());
        }
      }
      batchProcess (pcd_files, output_dir, sigma_s, sigma_r);
    }
    else
    {
      PCL_ERROR ("Batch processing mode enabled, but invalid input directory (%s) given!\n", input_dir.c_str ());
      return (-1);
    }
  }
}