3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: \href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.
翻译:3D点云登记是计算机视觉和机器人中的一个基本问题。 最近, 基于学习的点云登记方法取得了巨大的进步。 然而, 这些方法对外部线非常敏感, 导致更不正确的通信。 在本文中, 我们提议为点云登记建立一个全新的深图匹配框架。 具体地说, 我们首先将点云转换成图表, 并提取每个点的深度特征。 然后, 我们开发了一个基于深图匹配的模块, 以计算一个软通信矩阵。 通过使用图形匹配, 不仅每个点的本地几何, 而且在更大的范围内的其结构和地形学, 建立信件时会考虑, 以便找到更正确的通信。 我们用直接定义的通信损失来培训网络, 在测试阶段, 软通信转换成硬一对一的通信, 以便通过基于通信的解答器进行注册。 此外, 我们引入基于变压器的方法来生成图形构造的边缘, 从而进一步提高通信的质量。 在对象水平和场级基准数据设置上进行广泛的实验, 以便找到更准确的通信/ 。 在测试中, 拟议的方法能够实现 / http_ co/ co/ codeb/ 。 。 。 。