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arXiv · 2605.07607

FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth

Zhixin Cheng·Yujia Chen·Xujing Tao·Bohao Liao·Xiaotian Yin·Baoqun Yin·Tianzhu Zhang
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Abstract

Image-to-point cloud registration is often challenged by viewpoint changes, cross-modal discrepancies, and repetitive textures, which induce scale ambiguity and consequently lead to erroneous correspondences. Recent detection-free methods alleviate this issue by leveraging multi-scale features and transformer-based interactions. However, they still suffer from attention drift across layers and intra-scale inconsistencies, hindering precise registration. Inspired by human behavior, we propose a ``Focus--Sweep'' paradigm and develop a Hierarchical Focus--Sweep Interaction Module within an SSM-based framework to enhance multi-level cross-modal feature association. In addition, we introduce a Dynamic Layer Allocation Strategy that adaptively determines the iteration depth to better exploit geometric constraints and improve matching robustness. Extensive experiments and ablations on two benchmarks, RGB-D Scenes V2 and 7-Scenes, demonstrate that our approach achieves state-of-the-art performance.

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Zhixin Cheng, Yujia Chen, Xujing Tao, Bohao Liao, Xiaotian Yin, Baoqun Yin, Tianzhu Zhang. 2026-05-08. FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth. https://arxiv.org/abs/2605.07607

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