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

TL;DR

MCI-Net introduces a multi-domain context integration approach with graph-based and dynamic inlier selection modules to enhance point cloud registration accuracy and robustness, outperforming existing methods.

Contribution

The paper presents a novel multi-domain context integration network with graph neighborhood aggregation and dynamic inlier selection for improved point cloud registration.

Findings

Achieves 96.4% registration recall on 3DMatch dataset.

Outperforms state-of-the-art methods in accuracy and robustness.

Effective in indoor and outdoor point cloud registration scenarios.

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.