Closely adjacent object segmentation based on periphery-restrictive region growing

Abstract

Closely adjacent objects are a kind of challenging scene in three-dimensional point cloud segmentation. To address this issue, we propose a novel periphery-restrictive region-growing-based segmentation method. First, the clouds are smoothed by the moving least squares method and the periphery of the target clouds is extracted. Then an initial seed generation based on minimum curvature first of non-periphery points, namely, non-periphery seed generation algorithm is proposed. Finally, a nearest periphery restrictive growing algorithm is proposed for accurate segmentation. In addition, we establish a point cloud dataset of adjacent objects which contains three typically adjacent objects on the pipeline. We evaluate the effectiveness and accuracy of the proposed method on this dataset, and the extensive experiments show that the proposed method performs well on closely adjacent objects.