Abstract Plane detection is a key component for many applications, such as industrial reverse engineering and self-driving cars. However, existing plane-detection techniques are sensitive to noise and to user-defined parameters. We introduce a fast deterministic technique for plane detection in unorganized point clouds that is robust to noise and virtually independent of parameter tuning. It is based on a novel planarity test drawn from robust statistics and on a split and merge strategy. Its parameter values are automatically adjusted to fit the local distribution of samples in the input dataset, thus leading to good reconstruction of even small planar regions. We demonstrate the effectiveness of our solution on several real datasets, comparing its performance to state-of-art plane detection techniques, and showing that it achieves better accuracy, while still being one of the fastest.

中文翻译：

---

一种用于无组织点云平面检测的稳健统计方法

摘要 平面检测是许多应用的关键组件，例如工业逆向工程和自动驾驶汽车。然而，现有的平面检测技术对噪声和用户定义的参数很敏感。我们引入了一种快速确定性技术，用于无组织点云中的平面检测，该技术对噪声具有鲁棒性并且几乎独立于参数调整。它基于从稳健统计得出的新颖平面性测试以及拆分和合并策略。它的参数值会自动调整以适应输入数据集中样本的局部分布，从而即使很小的平面区域也能很好地重建。我们证明了我们的解决方案在几个真实数据集上的有效性，将其性能与最先进的平面检测技术进行了比较，