RegFormer++：一种高效的大规模3D激光雷达点云配准网络，具有投影感知的2D变换器
RegFormer++: An Efficient Large-Scale 3D LiDAR Point Registration Network with Projection-Aware 2D Transformer
AI总结：
本文提出RegFormer++，一种高效的3D点云配准网络，通过投影感知的2D变换器提升鲁棒性，无需额外后处理，实验表明在KITTI、NuScenes和Argoverse数据集上性能优异。
摘要：
尽管点云配准在物体级和室内场景中取得了显著进展，但大规模激光雷达配准方法之前很少被探索。挑战主要来自巨大的点数规模、复杂的点分布和户外激光雷达扫描中的大量异常值。此外，大多数现有配准工作通常采用两阶段范式：首先通过提取判别性局部描述符找到对应关系，然后利用鲁棒估计器（如RANSAC）过滤异常值，这高度依赖于精心设计的描述符和后处理选择。为了解决这些问题，我们提出了一种新颖的端到端微分变换器网络，称为RegFormer++，用于大规模点云对齐，而无需任何进一步的后处理。具体而言，提出了一种分层的投影感知2D变换器，具有线性复杂度，用于将原始激光雷达点投影到圆柱面上并提取全局点特征，这可以通过长距离依赖性提高对异常值的鲁棒性。由于我们将原始3D坐标填入2D投影位置，我们的设计变换器可以受益于2D处理的高效率和3D几何信息的准确性。此外，为了有效减少错误点匹配，设计了Bijective Association Transformer（BAT），结合交叉注意力和所有到所有点聚集。为了提高训练稳定性和鲁棒性，还设计了一个特征变换最优传输模块，用于回归最终姿态变换。在KITTI、NuScenes和Argoverse数据集上的大量实验表明，我们的模型在准确性和效率方面均达到了最先进的性能。
英文摘要：
Although point cloud registration has achieved remarkable advances in object-level and indoor scenes, large-scale LiDAR registration methods has been rarely explored before. Challenges mainly arise from the huge point scale, complex point distribution, and numerous outliers within outdoor LiDAR scans. In addition, most existing registration works generally adopt a two-stage paradigm: They first find correspondences by extracting discriminative local descriptors and then leverage robust estimators (e.g. RANSAC) to filter outliers, which are highly dependent on well-designed descriptors and post-processing choices. To address these problems, we propose a novel end-to-end differential transformer network, termed RegFormer++, for large-scale point cloud alignment without requiring any further post-processing. Specifically, a hierarchical projection-aware 2D transformer with linear complexity is proposed to project raw LiDAR points onto a cylindrical surface and extract global point features, which can improve resilience to outliers due to long-range dependencies. Because we fill original 3D coordinates into 2D projected positions, our designed transformer can benefit from both high efficiency in 2D processing and accuracy from 3D geometric information. Furthermore, to effectively reduce wrong point matching, a Bijective Association Transformer (BAT) is designed, combining both cross attention and all-to-all point gathering. To improve training stability and robustness, a feature-transformed optimal transport module is also designed for regressing the final pose transformation. Extensive experiments on KITTI, NuScenes, and Argoverse datasets demonstrate that our model achieves state-of-the-art performance in terms of both accuracy and efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
