Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale registration methods are rarely explored. Challenges mainly arise from the huge point number, complex distribution, and outliers of outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local features, and then leverage estimators (eg. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose an end-to-end transformer network (RegFormer) for large-scale point cloud alignment without any further post-processing. Specifically, a projection-aware hierarchical transformer is proposed to capture long-range dependencies and filter outliers by extracting point features globally. Our transformer has linear complexity, which guarantees high efficiency even for large-scale scenes. Furthermore, to effectively reduce mismatches, a bijective association transformer is designed for regressing the initial transformation. Extensive experiments on KITTI and NuScenes datasets demonstrate that our RegFormer achieves state-of-the-art performance in terms of both accuracy and efficiency.

翻译：尽管点云配准在目标级和室内场景中已取得显著进展，但大规模配准方法仍鲜有探索。其挑战主要源于室外激光雷达扫描数据的海量点数、复杂分布及异常值问题。此外，现有配准工作普遍采用两阶段范式：先通过提取判别性局部特征建立对应关系，再利用估计器（如RANSAC）滤除异常值，这高度依赖精心设计的描述子与后处理策略。为解决上述问题，我们提出一种无需任何后处理的端到端Transformer网络RegFormer，用于大规模点云对齐。具体而言，我们设计了投影感知分层Transformer，通过全局点特征提取捕捉长程依赖关系并滤除异常值。该Transformer具有线性复杂度，即使处理大规模场景也能保证高效率。此外，为有效减少误匹配，我们设计了双射关联Transformer用于回归初始变换矩阵。在KITTI和NuScenes数据集上的大量实验表明，RegFormer在精度与效率方面均达到当前最优水平。