论文/arXiv 2609.12417·1 个月前

谱一致性引导的低重叠场景多视角点云配准

针对低重叠多视角点云配准，GMPCR以谱一致性筛选对应与扫描对，并在多个数据集验证精度、鲁棒性和效率。

AI 整理

• 本体 SDK 与控制
• 感知与导航
• 端侧部署

原文链接：Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes（arXiv 2609.12417 · 发布日期：2026-09-11）

Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes

针对低重叠多视角点云配准，GMPCR以谱一致性筛选对应与扫描对，并在多个数据集验证精度、鲁棒性和效率。

核心要点

• 低重叠场景可靠对应有限，错误两两变换会影响全局位姿估计。
• GMPCR由初始对应构建二阶兼容结构，用主导谱响应评估对应可靠性与扫描对置信度。
• 筛选信息量高的扫描对形成稀疏位姿图，并结合最大团假设生成与自适应同步优化。
• 在3DMatch和3DLoMatch召回率达97.2%和89.6%，在ScanNet、ETH上保持竞争力，代码已公开。

验证范围与局限：验证范围为3DMatch、3DLoMatch、ScanNet和ETH点云配准实验。

英文摘要 (Abstract)

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.

作者：Tianyu Li, Yanghong Lin, Shudong Zhou, Kui Yang, Jingru Zhang, Li Fang, Wei Yao

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.12417：Spectral Consistency-Guided Multiview Point Cloud Registration for Low-Overlap Scenes访问 2026-10-21