发表机构
Wuhan University; Institute of Urban Environment, Chinese Academy of Sciences（武汉大学; 中国科学院城市环境研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出GMPCR，一种基于谱一致性引导的非学习多视角点云配准框架，通过谱响应评估对应可靠性与扫描对置信度，稀疏化姿态图，在低重叠场景下实现高效鲁棒配准，在3DMatch和3DLoMatch上分别达到97.2%和89.6%的召回率。
AI 中文摘要
多视角点云配准在低重叠场景中尤其具有挑战性，此时可靠的对应关系有限，且不正确的成对变换可能影响全局姿态估计。此外，配准所有扫描对计算开销大，因为许多扫描对仅提供微弱的几何信息。为解决这些问题，我们提出GMPCR，一种基于谱一致性引导的非学习框架，用于高效且鲁棒的多视角点云配准。GMPCR从初始对应关系构建精细的二阶兼容性结构，并利用其主导谱响应来评估对应关系可靠性和扫描对置信度。这使得在相对变换估计之前能够过滤不可靠的对应关系并选择信息丰富的扫描对，从而形成稀疏的姿态图并降低成对配准成本。对于每个保留的扫描对，采用基于最大团的假设生成来估计可靠的相对变换。所得姿态图通过自适应历史感知同步方案进一步优化，其中残差历史的影响根据全局旋转残差的变化进行调整。恢复机制还允许当全局一致性改善时，被降权的边重新获得置信度。在3DMatch、3DLoMatch、ScanNet和ETH上的实验证明了GMPCR的有效性。它在3DMatch和3DLoMatch上分别实现了97.2%和89.6%的配准召回率，同时在ScanNet和ETH上保持有竞争力的性能。结果表明，GMPCR在配准精度、对低重叠的鲁棒性和计算效率之间提供了良好的平衡。代码公开于该https URL。
英文摘要
Multiview point cloud registration is particularly challenging in low-overlap scenes, where reliable correspondences are limited and incorrect pairwise transformations can affect global pose estimation. In addition, registering all scan pairs is computationally expensive because many pairs provide weak geometric information. To address these problems, we propose GMPCR, a non-learning-based spectral consistency-guided framework for efficient and robust multiview point cloud registration. GMPCR builds a refined second-order compatibility structure from initial correspondences and uses its dominant spectral response to evaluate both correspondence reliability and scan-pair confidence. This allows unreliable correspondences to be filtered and informative scan pairs to be selected before relative transformation estimation, leading to a sparse pose graph and reduced pairwise registration cost. For each retained scan pair, maximal-clique-based hypothesis generation is used to estimate reliable relative transformations. The resulting pose graph is further refined by an adaptive history-aware synchronization scheme, in which the effect of residual history is adjusted according to changes in the global rotation residual. A recovery mechanism also allows down-weighted edges to regain confidence when their global consistency improves. Experiments on 3DMatch, 3DLoMatch, ScanNet, and ETH demonstrate the effectiveness of GMPCR. It achieves registration recalls of 97.2% and 89.6% on 3DMatch and 3DLoMatch, respectively, while maintaining competitive performance on ScanNet and ETH. The results show that GMPCR provides a favorable balance among registration accuracy, robustness to low overlap, and computational efficiency. The code is publicly available at https://github.com/swccj/gmpcr.
