Most studies on point cloud registration have established the problem in the case of ideal point cloud data. Although the state-of-the-art approaches have achieved amazing results on multiple public datasets, the issue of low overlap point cloud data invalidating state-of-the-art methods is acting as a latent challenge that has not been solved. Therefore, a profound analysis about why existing registration architectures break down in the low-overlap regime and how to select the appropriate strategies to improve the low overlap point cloud correspondence estimation is necessary and useful. Unfortunately, there are few survey works about low overlap cloud registration solving strategies and the corresponding datasets are very limited. This work briefly reviews mainstream deep learning-based point cloud registration and provides an in-depth analysis of the reasons why these architectures are not generalizable to scenarios with low overlapping areas. It is the first survey that mainly focuses on representative low overlap registration methods, their techniques, and related datasets for training/testing. It is worth noting that we also design and construct a large 3D dataset to eliminate the gap in Semantic-assisted point cloud registration with low overlap. Finally, challenges about low overlap point cloud registration and future directions in addressing these challenges are also pointed out.

中文翻译：

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基于深度学习的复杂场景低重叠点云配准：综述

大多数点云配准研究都在理想点云数据的情况下确定了该问题。尽管最先进的方法在多个公共数据集上取得了惊人的结果，但低重叠点云数据使最先进的方法失效的问题是一个尚未解决的潜在挑战。因此，深入分析现有配准架构为何在低重叠区域失效以及如何选择合适的策略来改进低重叠点云对应估计是必要且有用的。不幸的是，关于低重叠云配准解决策略的调查工作很少，相应的数据集也非常有限。这项工作简要回顾了主流的基于深度学习的点云配准，并深入分析了这些架构不能推广到低重叠区域场景的原因。这是第一项主要关注代表性低重叠配准方法、其技术以及用于训练/测试的相关数据集的调查。值得注意的是，我们还设计并构建了一个大型 3D 数据集，以消除低重叠的语义辅助点云配准中的差距。最后，还指出了低重叠点云配准的挑战以及解决这些挑战的未来方向。