Traditional point cloud registration methods require large overlap between scans, which imposes strict constraints on data acquisition. To facilitate registration, users have to carefully position scanners to ensure sufficient overlap. In this article, we propose to use high-level structural information (i.e., plane/line features and their interrelationship) for registration, which is capable of registering point clouds with small overlap, allowing more freedom in data acquisition. We design a novel plane-/line-based descriptor dedicated to establishing structure-level correspondences between point clouds. Based on this descriptor, we propose a simple but effective registration algorithm. We also provide a data set of real-world scenes containing a larger number of scans with a wide range of overlap. Experiments and comparisons with state-of-the-art methods on various data sets reveal that our method is superior to existing techniques. Though the proposed algorithm outperforms state-of-the-art methods on the most challenging data set, the point cloud registration problem is still far from being solved, leaving significant room for improvement and future work.

中文翻译：

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PLADE：用于小重叠点云配准的基于平面的描述符

传统的点云配准方法需要扫描之间有很大的重叠，这对数据采集施加了严格的限制。为了便于注册，用户必须小心地放置扫描仪以确保足够的重叠。在本文中，我们建议使用高级结构信息（即平面/线特征及其相互关系）进行配准，它能够配准重叠小的点云，从而在数据获取方面提供更大的自由度。我们设计了一个新颖的基于平面/线的描述符，专门用于建立点云之间的结构级对应关系。基于这个描述符，我们提出了一种简单但有效的配准算法。我们还提供了一个真实世界场景的数据集，其中包含大量具有广泛重叠的扫描。在各种数据集上与最先进方法的实验和比较表明，我们的方法优于现有技术。尽管所提出的算法在最具挑战性的数据集上优于最先进的方法，但点云配准问题仍远未解决，为改进和未来工作留下了很大的空间。