A Robust Feature-Matching Method for 3D Point Clouds via Spatial Encoding

This study addresses the challenging issues in 3D point cloud feature matching within the field of computer vision, where high data quality requirements and vulnerability to disturbances significantly impact performance. Existing methods are prone to outliers when generating feature correspondences due to noise, sampling deviations, symmetric structure, and other factors. To improve the robustness of point cloud feature matching, this paper proposes a novel 3D spatial encoding (3DSE) method that incorporates compact geometric constraints. Our method encodes the spatial layout of matching feature points by quantifying the order of appearance of matching points, and combines rigidity constraints to iteratively eliminate the least consistent matching pairs with the remaining point pairs, thereby sorting the initial matching set. The 3DSE algorithm was evaluated on multiple datasets, including simulated data, self-collected data, and public datasets, which cover data from both LiDAR and Kinect sensors. The comparison results with existing techniques demonstrate that 3DSE exhibits superior performance and robustness in handling noise, sparse point clouds, and changes in data modalities. The application of the proposed method significantly enhances the point cloud registration process, showing promising potential for 3D reconstruction, model-driven 3D object recognition, and pose estimation of non-cooperative targets.