Small fixes and improvements

- openni_klt.cpp: initialize counter_ to 0
- conversions.h: use std::size_t instead of uindex_t. This is an improvement when loading large clouds (e.g. more than 2^28 = 268 million XYZI points, because 2^28*16 overflows 32 bit unsigned, 16 being the point step/bytes per point). The user can switch uindex_t from 32 bit unsigned to 64 bit unsigned to solve this, but simply using std::size_t here is easier
- voxel_grid_covariance.h: construct array with appropriate size
- gicp.hpp: de-mean point neighbourhoods when computing covariance matrices
- ppf_registration.hpp: switch messages from info to debug
- region_growing.hpp: use auto for indices
- opennurbs_system.h: do not throw error when WIN32 is defined (see also https://github.com/microsoft/vcpkg/blob/master/ports/pcl/fix_opennurbs_win32.patch)
- octree_viewer.cpp: program name in help text
- openni2_viewer.cpp: remove unused variables

apps/src/openni_klt.cpp

@@ -112,7 +112,7 @@ class OpenNIViewer {
   using CloudConstPtr = typename Cloud::ConstPtr;
 
   OpenNIViewer(pcl::Grabber& grabber)
-  : grabber_(grabber), rgb_data_(nullptr), rgb_data_size_(0)
+  : grabber_(grabber), rgb_data_(nullptr), rgb_data_size_(0), counter_(0)
   {}
 
   void

common/include/pcl/conversions.h

@@ -198,10 +198,10 @@ namespace pcl
     else
     {
       // If not, memcpy each group of contiguous fields separately
-      for (uindex_t row = 0; row < msg.height; ++row)
+      for (std::size_t row = 0; row < msg.height; ++row)
       {
         const std::uint8_t* row_data = msg_data + row * msg.row_step;
-        for (uindex_t col = 0; col < msg.width; ++col)
+        for (std::size_t col = 0; col < msg.width; ++col)
         {
           const std::uint8_t* msg_data = row_data + col * msg.point_step;
           for (const detail::FieldMapping& mapping : field_map)

filters/include/pcl/filters/voxel_grid_covariance.h

@@ -464,7 +464,7 @@ namespace pcl
         }
 
         // Find k-nearest neighbors in the occupied voxel centroid cloud
-        Indices k_indices;
+        Indices k_indices (k);
         k = kdtree_.nearestKSearch (point, k, k_indices, k_sqr_distances);
 
         // Find leaves corresponding to neighbors

registration/include/pcl/registration/impl/gicp.hpp

@@ -62,10 +62,8 @@ GeneralizedIterativeClosestPoint<PointSource, PointTarget, Scalar>::computeCovar
   }
 
   Eigen::Vector3d mean;
-  pcl::Indices nn_indices;
-  nn_indices.reserve(k_correspondences_);
-  std::vector<float> nn_dist_sq;
-  nn_dist_sq.reserve(k_correspondences_);
+  pcl::Indices nn_indices(k_correspondences_);
+  std::vector<float> nn_dist_sq(k_correspondences_);
 
   // We should never get there but who knows
   if (cloud_covariances.size() < cloud->size())
@@ -85,20 +83,23 @@ GeneralizedIterativeClosestPoint<PointSource, PointTarget, Scalar>::computeCovar
 
     // Find the covariance matrix
     for (int j = 0; j < k_correspondences_; j++) {
-      const PointT& pt = (*cloud)[nn_indices[j]];
+      // de-mean neighbourhood to avoid inaccuracies when far away from origin
+      const double ptx = (*cloud)[nn_indices[j]].x - query_point.x,
+                   pty = (*cloud)[nn_indices[j]].y - query_point.y,
+                   ptz = (*cloud)[nn_indices[j]].z - query_point.z;
 
-      mean[0] += pt.x;
-      mean[1] += pt.y;
-      mean[2] += pt.z;
+      mean[0] += ptx;
+      mean[1] += pty;
+      mean[2] += ptz;
 
-      cov(0, 0) += pt.x * pt.x;
+      cov(0, 0) += ptx * ptx;
 
-      cov(1, 0) += pt.y * pt.x;
-      cov(1, 1) += pt.y * pt.y;
+      cov(1, 0) += pty * ptx;
+      cov(1, 1) += pty * pty;
 
-      cov(2, 0) += pt.z * pt.x;
-      cov(2, 1) += pt.z * pt.y;
-      cov(2, 2) += pt.z * pt.z;
+      cov(2, 0) += ptz * ptx;
+      cov(2, 1) += ptz * pty;
+      cov(2, 2) += ptz * ptz;
     }
 
     mean /= static_cast<double>(k_correspondences_);

registration/include/pcl/registration/impl/ppf_registration.hpp

@@ -83,9 +83,9 @@ pcl::PPFRegistration<PointSource, PointTarget>::computeTransformation(
   const std::vector<unsigned int> tmp_vec(aux_size, 0);
   std::vector<std::vector<unsigned int>> accumulator_array(input_->size(), tmp_vec);
 
-  PCL_INFO("Accumulator array size: %u x %u.\n",
-           accumulator_array.size(),
-           accumulator_array.back().size());
+  PCL_DEBUG("Accumulator array size: %u x %u.\n",
+            accumulator_array.size(),
+            accumulator_array.back().size());
 
   PoseWithVotesList voted_poses;
   // Consider every <scene_reference_point_sampling_rate>-th point as the reference
@@ -232,7 +232,7 @@ pcl::PPFRegistration<PointSource, PointTarget>::clusterPoses(
     typename pcl::PPFRegistration<PointSource, PointTarget>::PoseWithVotesList& poses,
     typename pcl::PPFRegistration<PointSource, PointTarget>::PoseWithVotesList& result)
 {
-  PCL_INFO("Clustering poses ...\n");
+  PCL_DEBUG("Clustering poses ...\n");
   // Start off by sorting the poses by the number of votes
   sort(poses.begin(), poses.end(), poseWithVotesCompareFunction);
 
@@ -268,9 +268,9 @@ pcl::PPFRegistration<PointSource, PointTarget>::clusterPoses(
   result.clear();
   std::size_t max_clusters = (clusters.size() < 3) ? clusters.size() : 3;
   for (std::size_t cluster_i = 0; cluster_i < max_clusters; ++cluster_i) {
-    PCL_INFO("Winning cluster has #votes: %d and #poses voted: %d.\n",
-             cluster_votes[cluster_i].second,
-             clusters[cluster_votes[cluster_i].first].size());
+    PCL_DEBUG("Winning cluster has #votes: %d and #poses voted: %d.\n",
+              cluster_votes[cluster_i].second,
+              clusters[cluster_votes[cluster_i].first].size());
     Eigen::Vector3f translation_average(0.0, 0.0, 0.0);
     Eigen::Vector4f rotation_average(0.0, 0.0, 0.0, 0.0);
     for (typename PoseWithVotesList::iterator v_it =

segmentation/include/pcl/segmentation/impl/region_growing.hpp

@@ -351,7 +351,7 @@ pcl::RegionGrowing<PointT, NormalT>::findPointNeighbours ()
   {
     for (int i_point = 0; i_point < point_number; i_point++)
     {
-      int point_index = (*indices_)[i_point];
+      const auto point_index = (*indices_)[i_point];
       neighbours.clear ();
       search_->nearestKSearch (i_point, neighbour_number_, neighbours, distances);
       point_neighbours_[point_index].swap (neighbours);
@@ -362,7 +362,7 @@ pcl::RegionGrowing<PointT, NormalT>::findPointNeighbours ()
     for (int i_point = 0; i_point < point_number; i_point++)
     {
       neighbours.clear ();
-      int point_index = (*indices_)[i_point];
+      const auto point_index = (*indices_)[i_point];
       if (!pcl::isFinite ((*input_)[point_index]))
         continue;
       search_->nearestKSearch (i_point, neighbour_number_, neighbours, distances);
@@ -386,7 +386,7 @@ pcl::RegionGrowing<PointT, NormalT>::applySmoothRegionGrowingAlgorithm ()
   {
     for (int i_point = 0; i_point < num_of_pts; i_point++)
     {
-      int point_index = (*indices_)[i_point];
+      const auto point_index = (*indices_)[i_point];
       point_residual[i_point].first = (*normals_)[point_index].curvature;
       point_residual[i_point].second = point_index;
     }
@@ -396,7 +396,7 @@ pcl::RegionGrowing<PointT, NormalT>::applySmoothRegionGrowingAlgorithm ()
   {
     for (int i_point = 0; i_point < num_of_pts; i_point++)
     {
-      int point_index = (*indices_)[i_point];
+      const auto point_index = (*indices_)[i_point];
       point_residual[i_point].first = 0;
       point_residual[i_point].second = point_index;
     }

surface/include/pcl/surface/3rdparty/opennurbs/opennurbs_system.h

@@ -137,15 +137,15 @@
 #if defined(_M_X64) && defined(WIN32) && defined(WIN64)
 // 23 August 2007 Dale Lear
 
-#if defined(_INC_WINDOWS)
+//#if defined(_INC_WINDOWS)
 // The user has included Microsoft's windows.h before opennurbs.h,
 // and windows.h has nested includes that unconditionally define WIN32.
 // Just undo the damage here or everybody that includes opennurbs.h after
 // windows.h has to fight with this Microsoft bug.
 #undef WIN32
-#else
-#error do not define WIN32 for x64 builds
-#endif
+//#else
+//#error do not define WIN32 for x64 builds
+//#endif
 
 // NOTE _WIN32 is defined for any type of Windows build
 #endif

tools/octree_viewer.cpp

@@ -459,8 +459,8 @@ int main(int argc, char ** argv)
 {
   if (argc != 3)
   {
-    std::cerr << "ERROR: Syntax is octreeVisu <pcd file> <resolution>" << std::endl;
-    std::cerr << "EXAMPLE: ./octreeVisu bun0.pcd 0.001" << std::endl;
+    std::cerr << "ERROR: Syntax is " << argv[0] << " <pcd file> <resolution>" << std::endl;
+    std::cerr << "EXAMPLE: ./" << argv[0] << " bun0.pcd 0.001" << std::endl;
     return -1;
   }
 

tools/openni2_viewer.cpp

@@ -275,10 +275,6 @@ class OpenNI2Viewer
   unsigned rgb_data_size_;
 };
 
-// Create the PCLVisualizer object
-pcl::visualization::PCLVisualizer::Ptr cld;
-pcl::visualization::ImageViewer::Ptr img;
-
 /* ---[ */
 int
 main (int argc, char** argv)