论文标题
FEC：点云分段的快速欧几里得聚类
FEC: Fast Euclidean Clustering for Point Cloud Segmentation
论文作者
论文摘要
从点云数据进行分割至关重要，例如遥感，移动机器人或自动驾驶汽车。但是，由3D范围传感器捕获的点云通常是稀疏且非结构化的，具有挑战性的有效分割。在本文中，我们提出了一个快速解决方案，以针对云实例分割，并具有较小的计算需求。为此，我们提出了一种新颖的快速欧几里得聚类（FEC）算法，该算法在现有作品中使用的聚类方案上应用了一个方案。我们的方法在概念上是简单，易于实现的（C ++中的40行），并且在产生高质量的结果的同时，针对经典分割方法实现了两个大小阶。
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.
