Letting the compiler auto-deduce template parameters

apps/src/openni_uniform_sampling.cpp

@@ -91,8 +91,8 @@ class OpenNIUniformSampling
       pcl::PointCloud<pcl::PointXYZRGBA> sampled;
       pass_.filter (sampled);
       *cloud_  = *cloud;
-      
-      pcl::copyPointCloud<pcl::PointXYZRGBA, pcl::PointXYZ> (sampled, *keypoints_);
+
+      pcl::copyPointCloud (sampled, *keypoints_);
     }
 
     void

common/include/pcl/common/impl/io.hpp

@@ -264,7 +264,7 @@ pcl::copyPointCloud (const pcl::PointCloud<PointInT> &cloud_in,
   // Do we want to copy everything? Remember we assume UNIQUE indices
   if (nr_p == cloud_in.points.size ())
   {
-    copyPointCloud<PointInT, PointOutT> (cloud_in, cloud_out);
+    copyPointCloud (cloud_in, cloud_out);
     return;
   }
 

doc/tutorials/content/sources/don_segmentation/don_segmentation.cpp

@@ -103,7 +103,7 @@ main (int argc, char *argv[])
 
   // Create output cloud for DoN results
   PointCloud<PointNormal>::Ptr doncloud (new pcl::PointCloud<PointNormal>);
-  copyPointCloud<PointXYZRGB, PointNormal>(*cloud, *doncloud);
+  copyPointCloud (*cloud, *doncloud);
 
   std::cout << "Calculating DoN... " << std::endl;
   // Create DoN operator

doc/tutorials/content/sources/random_sample_consensus/random_sample_consensus.cpp

@@ -88,7 +88,7 @@ main(int argc, char** argv)
   }
 
   // copies all inliers of the model computed to another PointCloud
-  pcl::copyPointCloud<pcl::PointXYZ>(*cloud, inliers, *final);
+  pcl::copyPointCloud (*cloud, inliers, *final);
 
   // creates the visualization object and adds either our original cloud or all of the inliers
   // depending on the command line arguments specified.

doc/tutorials/content/sources/registration_api/example2.cpp

@@ -60,10 +60,10 @@ estimateNormals (const PointCloud<PointXYZ>::Ptr &src,
   // For debugging purposes only: uncomment the lines below and use pcl_viewer to view the results, i.e.:
   // pcl_viewer normals_src.pcd
   PointCloud<PointNormal> s, t;
-  copyPointCloud<PointXYZ, PointNormal> (*src, s);
-  copyPointCloud<Normal, PointNormal> (normals_src, s);
-  copyPointCloud<PointXYZ, PointNormal> (*tgt, t);
-  copyPointCloud<Normal, PointNormal> (normals_tgt, t);
+  copyPointCloud (*src, s);
+  copyPointCloud (normals_src, s);
+  copyPointCloud (*tgt, t);
+  copyPointCloud (normals_tgt, t);
   savePCDFileBinary ("normals_src.pcd", s);
   savePCDFileBinary ("normals_tgt.pcd", t);
 }
@@ -154,10 +154,10 @@ computeTransformation (const PointCloud<PointXYZ>::Ptr &src,
 
   // Copy the data and save it to disk
 /*  PointCloud<PointNormal> s, t;
-  copyPointCloud<PointXYZ, PointNormal> (*keypoints_src, s);
-  copyPointCloud<Normal, PointNormal> (normals_src, s);
-  copyPointCloud<PointXYZ, PointNormal> (*keypoints_tgt, t);
-  copyPointCloud<Normal, PointNormal> (normals_tgt, t);*/
+  copyPointCloud (*keypoints_src, s);
+  copyPointCloud (normals_src, s);
+  copyPointCloud (*keypoints_tgt, t);
+  copyPointCloud (normals_tgt, t);*/
 
   // Find correspondences between keypoints in FPFH space
   CorrespondencesPtr all_correspondences (new Correspondences), 

examples/features/example_difference_of_normals.cpp

@@ -152,7 +152,7 @@ int main (int argc, char *argv[])
 
   // Create output cloud for DoN results
   PointCloud<PointOutT>::Ptr doncloud (new pcl::PointCloud<PointOutT>);
-  copyPointCloud<PointT, PointOutT>(*cloud, *doncloud);
+  copyPointCloud (*cloud, *doncloud);
 
   std::cout << "Calculating DoN... " << std::endl;
   // Create DoN operator

filters/include/pcl/filters/impl/morphological_filter.hpp

@@ -61,7 +61,7 @@ pcl::applyMorphologicalOperator (const typename pcl::PointCloud<PointT>::ConstPt
   if (cloud_in->empty ())
     return;
 
-  pcl::copyPointCloud<PointT, PointT> (*cloud_in, cloud_out);
+  pcl::copyPointCloud (*cloud_in, cloud_out);
 
   pcl::octree::OctreePointCloudSearch<PointT> tree (resolution);
 
@@ -116,7 +116,7 @@ pcl::applyMorphologicalOperator (const typename pcl::PointCloud<PointT>::ConstPt
     {
       pcl::PointCloud<PointT> cloud_temp;
 
-      pcl::copyPointCloud<PointT, PointT> (*cloud_in, cloud_temp);
+      pcl::copyPointCloud (*cloud_in, cloud_temp);
 
       for (size_t p_idx = 0; p_idx < cloud_temp.points.size (); ++p_idx)
       {

keypoints/include/pcl/keypoints/agast_2d.h

@@ -488,7 +488,7 @@ namespace pcl
         {
           pcl::PointCloud<pcl::PointUV> output_temp;
           detector->applyNonMaxSuppression (image_data, tmp_cloud, output_temp);
-          pcl::copyPointCloud<pcl::PointUV, Out> (output_temp, output);
+          pcl::copyPointCloud (output_temp, output);
         }
       };
 
@@ -516,7 +516,7 @@ namespace pcl
         {
           pcl::PointCloud<pcl::PointUV> output_temp;
           detector->detectKeypoints (image_data, output_temp);
-          pcl::copyPointCloud<pcl::PointUV, Out> (output_temp, output);
+          pcl::copyPointCloud (output_temp, output);
         }
       };
 

keypoints/include/pcl/keypoints/impl/brisk_2d.hpp

@@ -79,7 +79,7 @@ pcl::BriskKeypoint2D<PointInT, PointOutT, IntensityT>::detectKeypoints (PointClo
   brisk_scale_space.constructPyramid (image_data, width, height);
   pcl::PointCloud<pcl::PointWithScale> output_temp;
   brisk_scale_space.getKeypoints (threshold_, output_temp.points);
-  pcl::copyPointCloud<pcl::PointWithScale, PointOutT> (output_temp, output);
+  pcl::copyPointCloud (output_temp, output);
 
   // we do not change the denseness
   output.width = int (output.points.size ());

recognition/include/pcl/recognition/impl/cg/geometric_consistency.hpp

@@ -79,7 +79,7 @@ pcl::GeometricConsistencyGrouping<PointModelT, PointSceneT>::clusterCorresponden
 
   //temp copy of scene cloud with the type cast to ModelT in order to use Ransac
   PointCloudPtr temp_scene_cloud_ptr (new PointCloud ());
-  pcl::copyPointCloud<PointSceneT, PointModelT> (*scene_, *temp_scene_cloud_ptr);
+  pcl::copyPointCloud (*scene_, *temp_scene_cloud_ptr);
 
   pcl::registration::CorrespondenceRejectorSampleConsensus<PointModelT> corr_rejector;
   corr_rejector.setMaximumIterations (10000);

recognition/include/pcl/recognition/impl/cg/hough_3d.hpp

@@ -274,7 +274,7 @@ pcl::Hough3DGrouping<PointModelT, PointSceneT, PointModelRfT, PointSceneRfT>::cl
   // Insert maximas into result vector, after Ransac correspondence rejection
   // Temp copy of scene cloud with the type cast to ModelT in order to use Ransac
   PointCloudPtr temp_scene_cloud_ptr (new PointCloud);
-  pcl::copyPointCloud<PointSceneT, PointModelT> (*scene_, *temp_scene_cloud_ptr);
+  pcl::copyPointCloud (*scene_, *temp_scene_cloud_ptr);
 
   pcl::registration::CorrespondenceRejectorSampleConsensus<PointModelT> corr_rejector;
   corr_rejector.setMaximumIterations (10000);
@@ -355,7 +355,7 @@ pcl::Hough3DGrouping<PointModelT, PointSceneT, PointModelRfT, PointSceneRfT>::re
 
   //// Temp copy of scene cloud with the type cast to ModelT in order to use Ransac
   //PointCloudPtr temp_scene_cloud_ptr (new PointCloud);
-  //pcl::copyPointCloud<PointSceneT, PointModelT> (*scene_, *temp_scene_cloud_ptr);
+  //pcl::copyPointCloud (*scene_, *temp_scene_cloud_ptr);
 
   //for (size_t i = 0; i < model_instances.size (); ++i)
   //{

test/io/test_io.cpp

@@ -1036,7 +1036,7 @@ TEST (PCL, CopyPointCloud)
     cloud_b.points[i].rgba = 255;
   }
 
-  pcl::copyPointCloud<pcl::PointXYZ, pcl::PointXYZRGBA> (cloud_a, cloud_b);
+  pcl::copyPointCloud (cloud_a, cloud_b);
 
   for (size_t i = 0; i < cloud_a.points.size (); ++i)
   {
@@ -1047,7 +1047,7 @@ TEST (PCL, CopyPointCloud)
     cloud_a.points[i].x = cloud_a.points[i].y = cloud_a.points[i].z = 0;
   }
 
-  pcl::copyPointCloud<pcl::PointXYZRGBA, pcl::PointXYZ> (cloud_b, cloud_a);
+  pcl::copyPointCloud (cloud_b, cloud_a);
 
   for (size_t i = 0; i < cloud_a.points.size (); ++i)
   {