SVLA: A compact supervoxel segmentation method based on local allocation

Abstract

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.