Two-view point cloud registration network: feature and geometry.

Rigid point cloud registration is a crucial upstream task in computer vision, whose goal is to align two misaligned point clouds using a rigid transformation. Existing methods, which directly utilize extracted point features for computing point relationships, are likely to result in a wrong-matching relationship since two or more similar feature points in the source point cloud easily correspond to the same point in the target point cloud. To this end, this paper proposes a two-view point cloud registration network that better alleviates the problem of similar feature points from both the feature and geometry levels. Specifically, at the feature level, a residual correction unit is proposed to learn feature-aware coefficients from raw 3D point clouds to adaptively increase or decrease the difference between features. An attention mechanism is established in two-point clouds to capture the implicit feature relationship between the two-point clouds, gathering the information of another point cloud to enrich the feature information of its own point cloud. Second, at the geometry level, a dual-view graph topology fusion module is described. All points in the graph structure are no longer independent but connected by their geometry structure. Therefore, each point can aggregate neighbor information in the same point cloud and in different point clouds through the constructed single graph and interactive graph, so that each point can enhance the difference between points through its geometry structure. In order to fuse the information of a single graph and an interactive graph, a cross-attention module is proposed to supplement contextual information to obtain point features that are more suitable for matching. Experimental results demonstrate that our method achieves excellent results on complete and partial noisy point clouds. [ABSTRACT FROM AUTHOR]