Point Cloud Registration (PCR) is a critical and challenging task in computer vision. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. To optimize this high-order transformer further, we introduce an anchor node selection strategy. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify key correspondences for successful registration. In our experiments conducted on well-known datasets such as 3DMatch/3DLoMatch and KITTI, our approach has shown promising results, highlighting the effectiveness of our novel method.

翻译：点云配准（PCR）是计算机视觉中一项关键且具有挑战性的任务。其主要难点之一在于识别在不同扫描中具有一致语义与几何特性的显著且有意义的点。以往的方法由于整个点云中面片块之间的相似性以及缺乏对高效全局几何一致性的考虑，常面临模糊匹配的挑战。为解决这些问题，我们提出了一种包含多项创新技术的新框架。首先，我们引入了一种语义感知的几何编码器，它融合了对象级与面片级语义信息。该编码器通过减少面片级超点匹配中的模糊性，显著提升了配准召回率。此外，我们采用了一种先验知识方法，利用内在形状特征来识别显著点，从而能够提取场景中最显著的超级点与有意义的稠密点。其次，我们引入了一种创新的变换器，用于编码高阶几何特征。这些特征在考虑全局高阶几何一致性的同时，对于识别初始重叠区域内的显著点至关重要。为进一步优化该高阶变换器，我们引入了一种锚节点选择策略。通过基于这些锚节点编码帧间三角形或多面体一致性特征，我们可以有效学习显著超级点的高阶几何特征。随后，这些高阶特征被传播至稠密点，并利用Sinkhorn匹配模块识别关键对应关系，以实现成功的配准。在3DMatch/3DLoMatch与KITTI等知名数据集上的实验表明，我们的方法取得了显著成果，充分验证了该创新方法的有效性。