In the PCL tutorial, we can learn how to segment a plane and extract the Euclidean cluster point clouds. So now, after I used the `pcl::EuclideanClusterExtraction`

algorithm. I need the centroid or the mean position of each cluster.

Using `pcl::EuclideanClusterExtraction`

I need to calculate the centroid with the for loops. After my search I found out the `pcl::Kmeans`

which provides directly a function `get_centroids()`

to get the centroids of the clusters: https://pointclouds.org/documentation/classpcl_1_1_kmeans.html#a8788bd4098ea370e018119fc516a5eb4

Now, I’m a little bit confused. What is the real application different between `pcl::EuclideanClusterExtraction`

and `pcl::Kmeans`

? After analysing the source code, `pcl::EuclideanClusterExtraction`

provides us clusters based on three parameters. `pcl::Kmeans`

is used if we determine how many clusters we want to generate, because of the arguments in the constructor `Kmeans (unsigned int num_points, unsigned int num_dimensions)`

.

Is that true? Is there any other cases?

Source: Windows Questions C++