HyperGCT: A Dynamic Hyper-GNN-Learned Geometric Constraint for 3D Registration

• URL: http://arxiv.org/abs/2503.02195v1
• Date: Tue, 04 Mar 2025 02:05:43 GMT
• Title: HyperGCT: A Dynamic Hyper-GNN-Learned Geometric Constraint for 3D Registration
• Authors: Xiyu Zhang, Jiayi Ma, Jianwei Guo, Wei Hu, Zhaoshuai Qi, Fei Hui, Jiaqi Yang, Yanning Zhang,
• Abstract summary: We propose HyperGCT, a flexible dynamic Hyper-GNN-learned geometric constraint.<n>Our method is robust to graph noise, demonstrating a significant advantage in terms of generalization.
• Score: 60.01977041900338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 among matches. 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, our method is robust to graph noise, demonstrating a significant advantage in terms of generalization. The code will be released.

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