面向大规模室外点云配准的深度语义图匹配
Deep Semantic Graph Matching for Large-scale Outdoor Point Clouds Registration
AI总结：
针对点云配准忽略语义信息的问题，提出深度语义图匹配方法DeepSGM，通过构建语义邻接图并结合图卷积与注意力机制学习特征，将匹配转化为最优传输问题求解，在KITTI数据集上显著提升了大规模室外点云配准性能。
摘要：
当前的点云配准方法主要基于局部几何信息，通常忽略了场景中包含的语义信息。在本文中，我们将点云配准问题视为语义实例匹配与配准任务，并提出了一种用于大规模室外点云配准的深度语义图匹配方法（DeepSGM）。首先，使用语义分割网络获取3D点的语义类别标签。然后利用欧氏聚类算法将具有相同类别标签的相邻点聚类在一起以获取语义实例，这些实例由三种属性表示，包括空间位置信息、语义类别信息和全局几何形状信息。其次，基于语义实例的空间邻接关系构建语义邻接图。为了充分探索同一场景内和不同场景间语义实例的拓扑结构，利用图卷积网络学习空间分布特征和语义类别特征，并利用类似PointNet的网络学习全局几何形状特征。这三种特征进一步通过自注意力和交叉注意力机制进行增强。第三，将语义实例匹配表述为最优传输问题，并通过最优匹配层进行求解。最后，两幅点云之间的几何变换矩阵首先通过SVD算法进行估计，随后通过ICP算法进行细化。在KITTI Odometry数据集上进行的实验结果表明，所提出的方法提高了配准性能，并优于各种最先进的方法。
英文摘要：
Current point cloud registration methods are mainly based on local geometric information and usually ignore the semantic information contained in the scenes. In this paper, we treat the point cloud registration problem as a semantic instance matching and registration task, and propose a deep semantic graph matching method (DeepSGM) for large-scale outdoor point cloud registration. Firstly, the semantic categorical labels of 3D points are obtained using a semantic segmentation network. The adjacent points with the same category labels are then clustered together using the Euclidean clustering algorithm to obtain the semantic instances, which are represented by three kinds of attributes including spatial location information, semantic categorical information, and global geometric shape information. Secondly, the semantic adjacency graph is constructed based on the spatial adjacency relations of semantic instances. To fully explore the topological structures between semantic instances in the same scene and across different scenes, the spatial distribution features and the semantic categorical features are learned with graph convolutional networks, and the global geometric shape features are learned with a PointNet-like network. These three kinds of features are further enhanced with the self-attention and cross-attention mechanisms. Thirdly, the semantic instance matching is formulated as an optimal transport problem, and solved through an optimal matching layer. Finally, the geometric transformation matrix between two point clouds is first estimated by the SVD algorithm and then refined by the ICP algorithm. Experimental results conducted on the KITTI Odometry dataset demonstrate that the proposed method improves the registration performance and outperforms various state-of-the-art methods.
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。
