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直接看代码吧，基于PCL实现。库文件自己筛选以下吧

#include 
   
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                     using namespace std;void CreateCloudFromTxt(const std::string& file_path,pcl::PointCloud
                     
                      ::Ptr cloud){ std::ifstream file(file_path.c_str()); std::string line; std::string no_use; pcl::PointXYZ point; while (getline(file, line)) { string::iterator it; for (it = line.begin(); it < line.end(); it++) { if (*it == ',') { line.erase(it); line.insert(it, ' '); it--; } } std::stringstream ss(line); ss >> point.x; ss >> point.y; ss >> point.z; cloud->push_back(point); } file.close();}void visualization(pcl::PointCloud
                      
                       ::Ptr cloud){ boost::shared_ptr
                       
                         viewer(new pcl::visualization::PCLVisualizer("viewer")); pcl::visualization::PointCloudColorHandlerCustom
                        
                          single_color(cloud, 255, 0, 0); viewer->addPointCloud
                         
                          (cloud, single_color, "example"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 2, "example"); while (!viewer->wasStopped()) { viewer->spinOnce(100); boost::this_thread::sleep(boost::posix_time::microseconds(100000)); }}int main(int argc, char** argv){ pcl::PointCloud
                          
                           ::Ptr cloud(new pcl::PointCloud
                           
                            ); pcl::PointCloud 
                            
                             ::Ptr normals (new pcl::PointCloud 
                             
                              ); CreateCloudFromTxt(argv[1],cloud); pcl::search::Search
                              
                               ::Ptr tree = boost::shared_ptr
                               
                                
                                  > (new pcl::search::KdTree
                                 
                                  ); pcl::NormalEstimation
                                  
                                    normal_estimator; normal_estimator.setSearchMethod (tree); normal_estimator.setInputCloud (cloud); normal_estimator.setKSearch (20); normal_estimator.compute (*normals); pcl::RegionGrowing
                                   
                                     reg; reg.setMinClusterSize (50); reg.setMaxClusterSize (1000000); reg.setSearchMethod (tree); reg.setNumberOfNeighbours (30); reg.setInputCloud (cloud); //reg.setIndices (indices); reg.setInputNormals (normals); reg.setSmoothnessThreshold (3.0 / 180.0 * M_PI); reg.setCurvatureThreshold (1.0); std::vector 
                                    
                                      cluster_indices; reg.extract (cluster_indices); std::vector
                                     
                                      
                                       ::Ptr> Eucluextra; //用于储存欧式分割后的点云 for (std::vector
                                       
                                        ::const_iterator it = cluster_indices.begin (); it != cluster_indices.end (); ++it) { pcl::PointCloud
                                        
                                         ::Ptr cloud_cluster(new pcl::PointCloud
                                         
                                          ); for (std::vector
                                          
                                           ::const_iterator pit = it->indices.begin (); pit != it->indices.end (); pit++) cloud_cluster->points.push_back(cloud->points[*pit]); cloud_cluster->width = cloud_cluster->points.size (); cloud_cluster->height = 1; cloud_cluster->is_dense = true; Eucluextra.push_back(cloud_cluster); } for(int k=0;k
                                          
                                         
                                        
                                       
                                      
                                     
                                    
                                   
                                  
                                 
                                
                               
                              
                             
                            
                           
                          
                         
                        
                       
                      
                     
                    
                   
                  
                 
                
               
              
             
            
           
          
         
        
       
      
     
    
   

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