Fusion of Geometric Attention and Multi-Scale Feature Network for Point Cloud Registration

The different geometric features of point clouds affect the difficulty of point cloud registration. However, most point clouds have partial overlap, geometric features are disturbed by noise, and there are some indistinguishable features on the point cloud surface, which makes it more difficult to extract representative geometric features. Combining the above reasons, this paper proposes a point cloud registration network GMNet that fuses geometric attention and multi-scale features, using geometric Transformer to extract geometric features and encode point cloud point pairs with distances and angles to make it more robust under low overlap. Using multi-scale feature architecture to aggregate abundant semantic information at different scales to improve the accuracy of point cloud registration. Finally the features are selected by consistent voting algorithm with the appropriate domain size. The experimental results show that the overall registration accuracy of GMNet is higher, and the registration recall is improved to 93.4% and 76.0% for 3DMatch and 3DLoMatch datasets respectively. The relative rotation error and relative translation error are reduced to 6.2 cm and 0.26° on the KITTI dataset, respectively. The geometric Transformer used in this method extracts representative geometric features and combines multi-scale features to learn different levels of geometric information in the point cloud, which effectively improves the point cloud registration accuracy. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.