區域增長分割簇是錯誤的？

Question

我正在通過PCL點雲庫執行區域增長分割我的房間點雲。 彩色雲看起來如下：

正如你可以看到大部分的集羣看上去根據表面。 然而，當我告訴每個集羣separatedly，這些都是一些結果：

顯然簇不一樣的顏色的雲，但我不明白爲什麼。 我使用這個代碼到集羣存儲到分開的點雲：

//Store clusters into new pcls and all the clusters in an array of pcls 
    std::vector<pcl::PointCloud<pcl::PointXYZRGB>::Ptr> clusters_pcl; 
    for (int i = 0; i < clusters.size(); ++i) { 
      pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_cluster( 
          new pcl::PointCloud<pcl::PointXYZRGB>); 
      cloud_cluster->width = clusters[i].indices.size(); 
      cloud_cluster->height = 1; 
      cloud_cluster->is_dense = true; 
      for (int j = 0; j < clusters[i].indices.size(); ++j) { 
        //Take the corresponding point of the filtered cloud from the indices for the new pcl 
        cloud_cluster->push_back( 
            point_cloud_ptr->at(clusters[i].indices[j])); 
      } 
      indices2.clear(); 
      //pcl::removeNaNFromPointCloud(*cloud_cluster, *cloud_cluster, indices2); 
      clusters_pcl.push_back(cloud_cluster); 
    } 

是我的代碼是做錯事或者是區域增長分割的輸出實際上是正確的？

乾杯

-------------編輯-----------------

Here是點雲我我正在使用測試。

這裏越來越多的分割代碼的完整的區域，這是一個類似於教程：

std::vector<pcl::PointCloud<pcl::PointXYZRGB>::Ptr> region_growing_segmentation(
    pcl::PointCloud<pcl::PointXYZRGB>::Ptr point_cloud_ptr) { 
pcl::PointCloud<pcl::PointXYZRGB>& point_cloud = *point_cloud_ptr; 
std::vector<int> indices2; 
// Create the filtering object: downsample the dataset using a leaf size of 1cm 
pcl::VoxelGrid<pcl::PointXYZRGB> vg; 
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_filtered(
     new pcl::PointCloud<pcl::PointXYZRGB>); 
vg.setInputCloud(point_cloud_ptr); 
vg.setLeafSize(0.025f, 0.025f, 0.025f); 
vg.filter(*cloud_filtered); 
std::cout << "PointCloud after filtering has: " 
     << cloud_filtered->points.size() << " data points." << std::endl; 

pcl::search::Search<pcl::PointXYZRGB>::Ptr tree = boost::shared_ptr< 
     pcl::search::Search<pcl::PointXYZRGB> >(
     new pcl::search::KdTree<pcl::PointXYZRGB>); 
pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>); 
pcl::NormalEstimation<pcl::PointXYZRGB, pcl::Normal> normal_estimator; 
normal_estimator.setSearchMethod(tree); 
normal_estimator.setInputCloud(cloud_filtered); 
normal_estimator.setKSearch(50); 
normal_estimator.compute(*normals); 

pcl::RegionGrowing<pcl::PointXYZRGB, pcl::Normal> reg; 
reg.setMinClusterSize(50); 
reg.setMaxClusterSize(1000000); 
reg.setSearchMethod(tree); 
reg.setNumberOfNeighbours(100); 
reg.setInputCloud(cloud_filtered); 
reg.setInputNormals(normals); 
reg.setSmoothnessThreshold(5.0/180.0 * M_PI); 
reg.setCurvatureThreshold(1); 

std::vector<pcl::PointIndices> clusters; 
reg.extract(clusters); 
pcl::PointCloud<pcl::PointXYZRGB>::Ptr colored_cloud = 
     reg.getColoredCloud(); 
pcl::visualization::CloudViewer viewer("Cluster viewer"); 
viewer.showCloud(colored_cloud); 
while (!viewer.wasStopped()) { 
} 
std::vector<pcl::PointCloud<pcl::PointXYZRGB>::Ptr> clusters_pcl; 
for (int i = 0; i < clusters.size(); ++i) { 
pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud_cluster(
new pcl::PointCloud<pcl::PointXYZRGB>); 
cloud_cluster->width = clusters[i].indices.size(); 
cloud_cluster->height = 1; 
cloud_cluster->is_dense = true; 
for (int j = 0; j < clusters[i].indices.size(); ++j) { 
//Take the corresponding point of the filtered cloud from the indices for the new pcl 
cloud_cluster->push_back(
point_cloud_ptr->at(clusters[i].indices[j])); 
} 
indices2.clear(); 
//pcl::removeNaNFromPointCloud(*cloud_cluster, *cloud_cluster, indices2); 
clusters_pcl.push_back(cloud_cluster); 
} 

return clusters_pcl; 
} 

Answer 1

所以我就想通了，這是太簡單了，我看不見它;抱歉。 當我將點複製到羣集中時，我使用的是原始點雲，而不是過濾點。也許是因爲我甚至沒有想到這一點。

所以這個：

cloud_cluster->push_back(
       point_cloud_ptr->at(clusters[i].indices[j])); 

不得不被替換爲：

cloud_cluster->push_back(
       cloud_filtered->at(clusters[i].indices[j])); 

乾杯

Answer 2

試試這個代碼：

pcl::ExtractIndices<pcl::PointXYZRGB> extract; 
    std::vector<pcl::PointCloud<pcl::PointXYZRGB>::Ptr> output_clouds; //vector of point clouds that will hold the cluster clouds 

    for (int i = 0; i < clusters.size(); ++i){ 
     upcloud cloud_temp(new pcl::PointCloud<pcl::PointXYZRGB>); 

     //extract the cloud from the cluster indicies 
     extract.setInputCloud(input_cloud); 
     pcl::PointIndices cluster = clusters[i]; 
     boost::shared_ptr<pcl::PointIndices> indicies = boost::make_shared<pcl::PointIndices>(cluster); 
     extract.setIndices(indicies); 
     extract.setNegative(false); 
     extract.filter(*cloud_temp); 

     output_clouds.push_back(cloud_temp); 
    }