Comparison of 2D image models in segmentation performance for 3D laser point clouds

Abstract

The selection of a suitable representing model for 3D laser point clouds plays a significant role in 3D outdoor scene understanding. In this paper, we compare the segmentation performance of four types of models which can transform 3D laser point clouds into 2D images. In these models, fast optimal bearing-angle (FOBA) image is a novel 2D image model, which provides a general way to project 3D laser point clouds into 2D images. A series of segmentation performance tests and data analysis for these models are conducted in four datasets, which are acquired with different laser scanning modes. According to the experimental results, we argue that 2D image models greatly reduce the time cost of scene segmentation with a little loss of accuracy. Moreover, the usage of 2D image models is not limited in scene segmentation since robust features can be extracted from 2D image models to accomplish laser point classification and scene understanding.