Fast supervoxel segmentation of connectivity median simulation based on Manhattan distance

Supervoxels provide a more natural and compact 3D point cloud representation, which is a fundamental problem in point cloud processing and has attracted extensive attention from many scholars. At present, although the supervoxel segmentation has achieved significant results, the processing efficiency still restricts its further processing in the face of large-scale point cloud processing. Therefore, this paper proposes a new supervoxel segmentation method for point clouds to achieve fast supervoxel over-segmentation of large-scale point clouds while maintaining good boundaries and accuracy. Firstly, the supervoxel segmentation is formulated as a subset selection problem. With the idea of greedy strategy, the interval quick sorting and region growing methods is designed to realize the rapid merging of subsets in the iterative process. Secondly, the Manhattan distance metric and the seed point median simulation method are proposed to enhance the precision of iterative optimization and improve the efficiency of point cloud supervoxel segmentation. Experimental results show that, compared with state-of-the-art supervoxel segmentation method, the proposed method achieves the best results under UE, GCE, BR and other indicators, while reducing the running time of the algorithm by 10.6% to 28.3%. • We propose an interval sorting optimization method and Regional growth to reduce the computational steps, which improves the efficiency of supervoxel segmentation. • We introduce a Manhattan distance metric and design a median simulation method, which achieves a more accurate optimization of the clustering process. • The experiments show that the proposed algorithm achieves the state-of-the-art performance, especially, with efficiency gains of 10.6% to 28.3%.