Multispectral point cloud superpoint segmentation

The multitude of airborne point clouds limits the point cloud processing efficiency. Superpoints are grouped based on similar points, which can effectively alleviate the demand for computing resources and improve processing efficiency. However, existing superpoint segmentation methods focus only on local geometric structures, resulting in inconsistent spectral features of points within a superpoint. Such feature inconsistencies degrade the performance of subsequent tasks. Thus, this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation (GSI-SS). Specifically, a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented superpoints. Following the formation of the primary superpoints, an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is proposed. Experiments are conducted on two real multispectral point cloud datasets, and the proposed method achieved higher recall, precision, F score, and lower global consistency and feature classification errors. The experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.