提出IGASA框架，提升点云配准在噪声和遮挡下的精度与鲁棒性。
IGASA: Integrated Geometry-Aware and Skip-Attention Modules for Enhanced Point Cloud Registration
- 采用分层金字塔结构结合跳连注意力，增强多尺度特征对齐与局部几何一致性。
- 在3D(Lo)Match、KITTI和nuScenes等数据集上显著优于现有方法，提升明显。
- 适合自动驾驶、机器人等复杂场景下的高精度点云配准任务。
点云配准（PCR）是3D视觉中的基础任务，广泛应用于自动驾驶、机器人和环境建模。然而，现有方法在面对重噪声、显著遮挡和大范围变换时性能下降，导致配准精度不足。本文提出IGASA框架，基于分层金字塔架构（HPA），集成两个核心模块：分层跨层注意力（HCLA）和迭代几何感知精修（IGAR）。HCLA利用跳连注意力机制对齐多分辨率特征，增强局部几何一致性；IGAR在粗匹配建立可靠对应关系后，用于精细匹配阶段。该协同设计使IGASA能有效适应多样点云结构和复杂变换。在3D(Lo)Match、KITTI、nuScenes等四个主流基准数据集上评估，实验结果表明，IGASA持续超越当前最优方法，显著提升注册精度。本工作为点云配准技术发展提供坚实基础，并为实际3D视觉应用提供重要参考。代码已开源。
原文摘要 · Abstract (English)
Point cloud registration (PCR) is a fundamental task in 3D vision and provides essential support for applications such as autonomous driving, robotics, and environmental modeling. Despite its widespread use, existing methods often fail when facing real-world challenges like heavy noise, significant occlusions, and large-scale transformations. These limitations frequently result in compromised registration accuracy and insufficient robustness in complex environments. In this paper, we propose IGASA as a novel registration framework constructed upon a Hierarchical Pyramid Architecture (HPA) designed for robust multi-scale feature extraction and fusion. The framework integrates two pivotal components consisting of the Hierarchical Cross-Layer Attention (HCLA) module and the Iterative Geometry-Aware Refinement (IGAR) module. The HCLA module utilizes skip attention mechanisms to align multi-resolution features and enhance local geometric consistency. Simultaneously, the IGAR module is designed for the fine matching phase by leveraging reliable correspondences established during coarse matching. This synergistic integration within the architecture allows IGASA to adapt effectively to diverse point cloud structures and intricate transformations. We evaluate the performance of IGASA on four widely recognized benchmark datasets including 3D(Lo)Match, KITTI, and nuScenes. Our extensive experiments consistently demonstrate that IGASA significantly surpasses state-of-the-art methods and achieves notable improvements in registration accuracy. This work provides a robust foundation for advancing point cloud registration techniques while offering valuable insights for practical 3D vision applications. The code for IGASA is available in \href{https://github.com/DongXu-Zhang/IGASA}{https://github.com/DongXu-Zhang/IGASA}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。