Supervoxels provide a more natural and compact representation of three dimensional point clouds, and enable the operations to be performed on regions rather than on the scattered points. Many state-of-the-art supervoxel segmentation methods adopt fixed resolution for each supervoxel, and rely on the initialization of seed points. As a result, they may not preserve well the boundaries of the point cloud with a non-uniform density. In this paper, we present a simple but effective supervoxel segmentation method for point clouds, which formalizes supervoxel segmentation as a subset selection problem. We develop an heuristic algorithm that utilizes local information to efficiently solve the subset selection problem. The proposed method can produce supervoxels with adaptive resolutions, and dose not rely the selection of seed points. The method is fully tested on three publicly available point cloud segmentation benchmarks, which cover the major point cloud types. The experimental results show that compared with the state-of-the-art supervoxel segmentation methods, the supervoxels extracted using our method preserve the object boundaries and small structures more effectively, which is reflected in a higher boundary recall and lower under-segmentation error.

超体素提供了更自然，更紧凑的三维点云表示，使操作可以在区域上执行，而不是在分散点上执行。许多最先进的超体素分割方法对每个超体素采用固定分辨率，并依赖于种子点的初始化。结果，它们可能无法很好地保留具有不均匀密度的点云的边界。在本文中，我们提出了一种简单而有效的点云超体素分割方法，该方法将超体素分割形式化为子集选择问题。我们开发了一种启发式算法，该算法利用本地信息来有效解决子集选择问题。所提出的方法可以产生具有自适应分辨率的超体素，并且不依赖于种子点的选择。该方法已在涵盖主要点云类型的三个可公开获得的点云细分基准上进行了全面测试。实验结果表明，与最新的超体素分割方法相比，使用我们的方法提取的超体素可以更有效地保留对象边界和较小的结构，这体现在更高的边界召回率和更低的分割不足误差上。