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.

翻译：从点云数据分离对于遥感、移动机器人或自主汽车等许多应用至关重要。 但是, 3D射程传感器所捕捉的点云通常稀少且没有结构, 具有挑战性的高效分割。 在本文中, 我们提出了一个快速解决方案, 以小的计算需求来点云体分解。 为此, 我们提出一个新的快速的 Euclidean 群集算法( FEC) 算法, 对现有工程所使用的集束方案应用一个点性计划。 我们的方法在概念上很简单, 容易执行( C++ 40 行), 并且比经典的分解方法更快地达到两个数量级, 同时产生高质量的结果 。