A Partial-to-Partial Point Cloud Registration Method Based on Geometric Attention Network

Figure 2

The network architecture of GAP-Net proposed in this paper. The upper line is the overall structure of the entire network, and the lower line is the composition of each module. To extract superpoints and aggregate their features, the KPConv-SSA backbone network is used to downsample the input point clouds. The overlapping attention module, guided by geometric information, is applied to superpoints to encode the information of the point clouds and infer the overlapping regions. Finally, RANSAC is used for registration.