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%.

中文翻译：

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基于曼哈顿距离的连通性中位仿真快速超级体素分割


超级体素提供了更自然、更紧凑的 3D 点云表示，这是点云处理中的一个基本问题，引起了众多学者的广泛关注。目前，尽管超级体素分割取得了显著的成果，但面对大规模的点云处理，处理效率仍然制约了其进一步处理。因此，本文提出了一种新的点云超级体素分割方法，以实现大规模点云的快速超级体素过分割，同时保持良好的边界和精度。首先，将超级体素分割表述为子集选择问题;该文以贪婪策略的思想，设计了区间快速排序和区域增长方法，实现了迭代过程中子集的快速合并。其次，提出了曼哈顿距离度量和种子点中位数模拟方法，以增强迭代优化的精度，提高点云超级体素分割的效率;实验结果表明，与最先进的超级体素分割方法相比，所提方法在 UE、GCE、BR 等指标下取得了最佳结果，同时将算法的运行时间缩短了 10.6% 至 28.3%。