  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.
