mlsanityData to 14 Oct 2026

CA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection

arXiv 2506.21364 ↗First posted 26 Jun 2025cs.CV cs.AIcited by 8

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Abstract (arXiv)

Detection-free methods typically follow a coarse-to-fine pipeline, extracting image and point cloud features for patch-level matching and refining dense pixel-to-point correspondences. However, differences in feature channel attention between images and point clouds may lead to degraded matching results, ultimately impairing registration accuracy. Furthermore, similar structures in the scene could lead to redundant correspondences in cross-modal matching. To address these issues, we propose Channel Adaptive Adjustment Module (CAA) and Global Optimal Selection Module (GOS). CAA enhances intra-modal features and suppresses cross-modal sensitivity, while GOS replaces local selection with global optimization. Experiments on RGB-D Scenes V2 and 7-Scenes demonstrate the superiority of our method, achieving state-of-the-art performance in image-to-point cloud registration.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 41 of its 68 references that are in the map. First posted on arXiv 26 Jun 2025.

Builds on

41 of 68 references.

References · 41

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29 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 8.

8 papers.