用动态超图学习3D点云匹配的几何约束，提升注册精度与鲁棒性。
HyperGCT: A Dynamic Hyper-GNN-Learned Geometric Constraint for 3D Registration
- 通过动态超图聚合对应点特征，挖掘高阶一致性关系。
- 在3DMatch等数据集上达到当前最优性能，噪声下仍保持稳定。
- 适合需要高精度与抗噪能力的3D重建、SLAM应用。
3D点云配准中，特征匹配间的几何约束至关重要。现有方法通常将无序匹配建模为一致性图，并采样一致匹配生成假设。然而显式图构建会引入噪声，使手工设计的几何约束难以有效判断一致性。为此，我们提出HyperGCT，一种灵活的动态超图神经网络学习几何约束方法，利用3D对应关系间的高阶一致性。据我们所知，HyperGCT是首个从动态超图中挖掘鲁棒几何约束用于3D注册的方法。通过动态优化超图中的顶点与边特征聚合，HyperGCT有效捕捉对应关系间的相关性，实现精确假设生成。在3DMatch、3DLoMatch、KITTI-LC和ETH上的大量实验表明，HyperGCT达到领先性能。此外，其对图噪声具有强鲁棒性，在泛化能力方面表现显著优势。
原文摘要 · Abstract (English)
Geometric constraints between feature matches are critical in 3D point cloud registration problems. Existing approaches typically model unordered matches as a consistency graph and sample consistent matches to generate hypotheses. However, explicit graph construction introduces noise, posing great challenges for handcrafted geometric constraints to render consistency. To overcome this, we propose HyperGCT, a flexible dynamic Hyper-GNN-learned geometric ConstrainT that leverages high-order consistency among 3D correspondences. To our knowledge, HyperGCT is the first method that mines robust geometric constraints from dynamic hypergraphs for 3D registration. By dynamically optimizing the hypergraph through vertex and edge feature aggregation, HyperGCT effectively captures the correlations among correspondences, leading to accurate hypothesis generation. Extensive experiments on 3DMatch, 3DLoMatch, KITTI-LC, and ETH show that HyperGCT achieves state-of-the-art performance. Furthermore, HyperGCT is robust to graph noise, demonstrating a significant advantage in terms of generalization.
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