MCI-Net: A Robust Multi-Domain Context Integration Network for Point Cloud Registration

• URL: http://arxiv.org/abs/2512.23472v1
• Date: Mon, 29 Dec 2025 13:55:33 GMT
• Title: MCI-Net: A Robust Multi-Domain Context Integration Network for Point Cloud Registration
• Authors: Shuyuan Lin, Wenwu Peng, Junjie Huang, Qiang Qi, Miaohui Wang, Jian Weng,
• Abstract summary: We propose a multi-domain context integration network (MCI-Net) that improves feature representation and registration performance.<n>Specifically, we propose a graph neighborhood aggregation module, which constructs a global graph to capture the overall structural relationships within point clouds.<n>We then propose a progressive context interaction module to enhance feature discriminability.<n>Experiments on indoor RGB-D and outdoor LiDAR datasets show that the proposed MCI-Net significantly outperforms existing state-of-the-art methods.
• Score: 28.6535442193107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robust and discriminative feature learning is critical for high-quality point cloud registration. However, existing deep learning-based methods typically rely on Euclidean neighborhood-based strategies for feature extraction, which struggle to effectively capture the implicit semantics and structural consistency in point clouds. To address these issues, we propose a multi-domain context integration network (MCI-Net) that improves feature representation and registration performance by aggregating contextual cues from diverse domains. Specifically, we propose a graph neighborhood aggregation module, which constructs a global graph to capture the overall structural relationships within point clouds. We then propose a progressive context interaction module to enhance feature discriminability by performing intra-domain feature decoupling and inter-domain context interaction. Finally, we design a dynamic inlier selection method that optimizes inlier weights using residual information from multiple iterations of pose estimation, thereby improving the accuracy and robustness of registration. Extensive experiments on indoor RGB-D and outdoor LiDAR datasets show that the proposed MCI-Net significantly outperforms existing state-of-the-art methods, achieving the highest registration recall of 96.4\% on 3DMatch. Source code is available at http://www.linshuyuan.com.

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