  Segmentation from point cloud data is essential in many applications such as
remote sensing, mobile robots, or autonomous cars. However, the point clouds
captured by the 3D range sensor are commonly sparse and unstructured,
challenging efficient segmentation. In this paper, we present a fast solution
to point cloud instance segmentation with small computational demands. To this
end, we propose a novel fast Euclidean clustering (FEC) algorithm which applies
a pointwise scheme over the clusterwise scheme used in existing works. Our
approach is conceptually simple, easy to implement (40 lines in C++), and
achieves two orders of magnitudes faster against the classical segmentation
methods while producing high-quality results.
