With the development of three-dimensional (3D) point cloud acquisition technologies, supervoxels have become increasingly important, as they provide compact and uniform representations. In this study, a novel supervoxel segmentation method, supervoxels based on local allocation (SVLA), is proposed. SVLA is composed of three steps, namely extreme point determination, local allocation (LA), and connectivity insurance. LA defines a novel cost function for preserving instance boundaries and enforces local minimization. To test the performance of SVLA, the non-compactness error (NCE) is newly defined to evaluate the compactness, and three commonly used evaluation metrics are employed. Both indoor and outdoor datasets are utilized to perform the experiments. Based on the visual and quantitative analysis of the segmentation results, SVLA demonstrates fulfillment of boundary adherence, compact constraints, and low computational complexity. Compared to state-of-the-art algorithms, SVLA yields superior results, especially with regard to indoor point clouds.

随着三维（3D）点云采集技术的发展，超体素变得越来越重要，因为它们提供了紧凑而统一的表示形式。在这项研究中，提出了一种新的超体素分割方法，基于局部分配的超体素（SVLA）。SVLA由三个步骤组成，即极端点确定，本地分配（LA）和连接性保险。LA定义了一种新颖的成本函数来保留实例边界并强制执行本地最小化。为了测试SVLA的性能，新定义了非紧凑度误差（NCE）以评估紧凑性，并采用了三种常用的评估指标。室内和室外数据集均用于执行实验。在对分割结果进行视觉和定量分析的基础上，SVLA演示了边界遵守，紧凑约束和低计算复杂性的实现。与最先进的算法相比，SVLA产生更好的结果，尤其是在室内点云方面。