Research Article

DKGCN-PCR: Deformable Kernel Graph Convolutional Network for Point Cloud Registration

¹ Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, PR China
² 77123 units of PLA, Mianyang 621000, PR China

^* Corresponding authors: This email address is being protected from spambots. You need JavaScript enabled to view it. (L.L.), This email address is being protected from spambots. You need JavaScript enabled to view it. (Z.L.)

Received: 14 July 2025
Accepted: 12 August 2025

Abstract

We study the problem of feature extraction in point cloud registration. Traditional point clouds has the characteristic of irregular structure, which causes the neighborhood relationship that cannot effectively obtain point cloud data, and increases the difficulty of feature extraction in the point cloud registration task. This paper proposes a graph convolution point cloud registration network based on a deformable kernel. Compared with the non-deformable kernel, the proposed network is more suitable for irregular and unstructured point cloud data. Meanwhile, the network uses the semantic residual module to restore the lost local information and enhance the integrity of feature expression. The feature fusion layer integrates global and local features to enhance the model’s ability to express the features of complex point cloud data. We conducted tests on the 3DMatch, 3DLoMatch, and KITTI datasets to verify the effectiveness of the algorithm.