MCI-Net：一种用于点云配准的鲁棒多域上下文整合网络
MCI-Net: A Robust Multi-Domain Context Integration Network for Point Cloud Registration
AI总结：
MCI-Net通过多域上下文整合提升点云配准性能，采用图邻域聚合、渐进式上下文交互和动态内点选择方法，在3DMatch数据集上达到96.4%的配准召回率。
摘要：
鲁棒且判别性的特征学习对于高质量点云配准至关重要。然而，现有基于深度学习的方法通常依赖于欧几里得邻域策略进行特征提取，这在有效捕捉点云中的隐含语义和结构一致性方面存在困难。为了解决这些问题，我们提出了一种多域上下文整合网络（MCI-Net），通过聚合来自不同领域的上下文线索来改进特征表示和配准性能。具体来说，我们提出了一种图邻域聚合模块，构建一个全局图以捕捉点云中的整体结构关系。然后，我们提出了一种渐进式上下文交互模块，通过执行领域内特征解耦和跨领域上下文交互来增强特征判别性。最后，我们设计了一种动态内点选择方法，通过利用姿态估计多轮次中的残差信息优化内点权重，从而提高配准的准确性和鲁棒性。在室内RGB-D和室外LiDAR数据集上的大量实验表明，所提出的MCI-Net显著优于现有最先进方法，在3DMatch上实现了96.4%的配准召回率。源代码可在http://www.linshuyuan.com获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
