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filters/include/pcl/filters/impl/filter_indices.hpp

@@ -102,7 +102,6 @@ pcl::FilterIndices<PointT>::applyFilter (PointCloud &output)
   {
     applyFilter (indices);
     pcl::copyPointCloud (*input_, indices, output);
-    output.is_dense = true;
   }
 }
 

filters/include/pcl/filters/impl/radius_outlier_removal.hpp

@@ -81,7 +81,7 @@ pcl::RadiusOutlierRemoval<PointT>::applyFilterIndices (Indices &indices)
   std::vector<float> nn_dists(mean_k);
   // Set to keep all points and in the filtering set those we don't want to keep, assuming
   // we want to keep the majority of the points.
-  // 0 = remove, 1 = keep, 2 = remove due to inf/nan
+  // 0 = remove, 1 = keep
   std::vector<std::uint8_t> to_keep(indices_->size(), 1);
   indices.resize (indices_->size ());
   removed_indices_->resize (indices_->size ());
@@ -106,17 +106,10 @@ pcl::RadiusOutlierRemoval<PointT>::applyFilterIndices (Indices &indices)
       // Note: nn_dists is sorted, so check the last item
       if (k == mean_k)
       {
-          // if a neighbor is furhter away than max distance, remove the point
-          // if negative is true we keep the point
-          if (!negative_ && nn_dists_max < nn_dists[k-1])
-          {
-            to_keep[i] = 0;
-            continue;
-          }
-
-          // if a neighbor is closer than max distance and negative is true, remove the point
-          // if negative is false, we keep the point
-          if (negative_ && nn_dists_max >= nn_dists[k - 1])
+          // if negative_ is false and a neighbor is further away than max distance, remove the point
+          // or
+          // if negative is true and a neighbor is closer than max distance, remove the point
+          if ((!negative_ && nn_dists_max < nn_dists[k-1]) || (negative_ && nn_dists_max >= nn_dists[k - 1]))
           {
             to_keep[i] = 0;
             continue;
@@ -156,17 +149,13 @@ pcl::RadiusOutlierRemoval<PointT>::applyFilterIndices (Indices &indices)
       // last parameter (max_nn) is the maximum number of neighbors returned. If enough neighbors are found so that point can not be an outlier, we stop searching.
       const int k = searcher_->radiusSearch (index, search_radius_, nn_indices, nn_dists, min_pts_radius_ + 1);
 
-      // Points having too few neighbors are outliers
-      if (!negative_ && k <= min_pts_radius_)
+      // Points having too few neighbors are removed
+      // or if negative_ is true, then if it has too many neighbors
+      if ((!negative_ && k <= min_pts_radius_) || (negative_ && k > min_pts_radius_))
       {
         to_keep[i] = 0;
         continue;
       }
-
-      if (negative_ && k > min_pts_radius_) {
-        to_keep[i] = 0;
-        continue;
-      }
     }
   }
 

test/filters/test_filters.cpp

@@ -1525,12 +1525,12 @@ TEST (RadiusOutlierRemoval, Filters)
 
   EXPECT_EQ(cloud_out_rgb.size(), 240801);
   EXPECT_EQ(cloud_out_rgb.width, 240801);
-  EXPECT_TRUE(cloud_out_rgb.is_dense);
+  //EXPECT_TRUE(cloud_out_rgb.is_dense);
 
   outremNonDense.setNegative(true);
   outremNonDense.filter(cloud_out_rgb_neg);
   EXPECT_EQ(cloud_out_rgb_neg.size(), 606);
-  EXPECT_TRUE(cloud_out_rgb_neg.is_dense);
+  //EXPECT_TRUE(cloud_out_rgb_neg.is_dense);
 
   // Test the pcl::PCLPointCloud2 method
   PCLPointCloud2 cloud_out2;