标题： LPRnet：一种用于激光雷达和航拍点云的自监督配准网络 显示英文标题

标题： LPRnet: A self-supervised registration network for LiDAR and photogrammetric point clouds

摘要： LiDAR和摄影测量分别是主动和被动的远程传感技术，用于点云获取，分别提供了互补的优势和异构性。 由于感知机制、空间分布和坐标系统的根本差异，它们的点云在密度、精度、噪声和重叠方面表现出显著的差异。 加上大规模场景缺乏真实数据，整合异构点云是一项极具挑战性的任务。 本文提出了一种基于掩码自编码器的自监督配准网络，专注于异构LiDAR和摄影测量点云。 其核心方法引入了一种多尺度掩码训练策略，在自监督下从异构点云中提取鲁棒特征。 为了进一步提高配准性能，设计了一个旋转-平移嵌入模块，以有效捕捉精确刚体变换的关键特征。 在鲁棒表示的基础上，一种基于Transformer的架构无缝集成了局部和全局特征，促进了在不同点云数据集上的精确对齐。 所提出的方法展示了对LiDAR和摄影测量点云的强大特征提取能力，解决了在场景级别获取真实数据的挑战。 在两个真实数据集上进行的实验验证了所提方法在解决异构点云配准问题上的有效性。 显示英文摘要

摘要： LiDAR and photogrammetry are active and passive remote sensing techniques for point cloud acquisition, respectively, offering complementary advantages and heterogeneous. Due to the fundamental differences in sensing mechanisms, spatial distributions and coordinate systems, their point clouds exhibit significant discrepancies in density, precision, noise, and overlap. Coupled with the lack of ground truth for large-scale scenes, integrating the heterogeneous point clouds is a highly challenging task. This paper proposes a self-supervised registration network based on a masked autoencoder, focusing on heterogeneous LiDAR and photogrammetric point clouds. At its core, the method introduces a multi-scale masked training strategy to extract robust features from heterogeneous point clouds under self-supervision. To further enhance registration performance, a rotation-translation embedding module is designed to effectively capture the key features essential for accurate rigid transformations. Building upon the robust representations, a transformer-based architecture seamlessly integrates local and global features, fostering precise alignment across diverse point cloud datasets. The proposed method demonstrates strong feature extraction capabilities for both LiDAR and photogrammetric point clouds, addressing the challenges of acquiring ground truth at the scene level. Experiments conducted on two real-world datasets validate the effectiveness of the proposed method in solving heterogeneous point cloud registration problems.