基于对比学习的跨模态信息引导网络用于点云配准
Cross-Modal Information-Guided Network using Contrastive Learning for Point Cloud Registration
AI总结：
本文提出跨模态信息引导网络CMIGNet，通过融合2D投影图像与3D点云特征，结合重叠对比学习和跨模态对比学习，并利用掩码预测模块识别关键点，在多个基准数据集上实现了精确且鲁棒的点云配准。
摘要：
目前大多数点云配准方法依赖于从点中提取特征。然而，这些方法受限于对单一模态点信息的依赖，可能导致全局特征感知不足和纹理信息缺乏等问题。实际上，人类可以利用从2D图像中学习到的视觉信息来理解3D世界。基于这一事实，我们提出了一种新颖的跨模态信息引导网络（CMIGNet），该网络通过跨模态信息获取全局形状感知，以实现精确且鲁棒的点云配准。具体来说，我们首先引入点云的投影图像，并利用注意力机制融合跨模态特征。此外，我们采用了两种对比学习策略，即重叠对比学习和跨模态对比学习。前者关注重叠区域的特征，后者强调2D与3D特征之间的对应关系。最后，我们提出了一个掩码预测模块来识别点云中的关键点。在多个基准数据集上的大量实验表明，我们的网络实现了优越的配准性能。
英文摘要：
The majority of point cloud registration methods currently rely on extracting features from points. However, these methods are limited by their dependence on information obtained from a single modality of points, which can result in deficiencies such as inadequate perception of global features and a lack of texture information. Actually, humans can employ visual information learned from 2D images to comprehend the 3D world. Based on this fact, we present a novel Cross-Modal Information-Guided Network (CMIGNet), which obtains global shape perception through cross-modal information to achieve precise and robust point cloud registration. Specifically, we first incorporate the projected images from the point clouds and fuse the cross-modal features using the attention mechanism. Furthermore, we employ two contrastive learning strategies, namely overlapping contrastive learning and cross-modal contrastive learning. The former focuses on features in overlapping regions, while the latter emphasizes the correspondences between 2D and 3D features. Finally, we propose a mask prediction module to identify keypoints in the point clouds. Extensive experiments on several benchmark datasets demonstrate that our network achieves superior registration performance.
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。
