  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 competitive performance in terms of
both accuracy and efficiency.
