A review of road 3D modeling based on light detection and ranging point clouds

Abstract

Increasing development of accurate and efficient road three-dimensional (3D) modeling presents great opportunities to improve the data exchange and integration of building information modeling (BIM) models. 3D modeling of road scenes is crucial for reference in asset management, construction, and maintenance. Light detection and ranging (LiDAR) technology is increasingly employed to generate high-quality point clouds for road inventory. In this paper, we specifically investigate the use of LiDAR data for road 3D modeling. The purpose of this review is to provide references about the existing work on the road 3D modeling based on LiDAR point clouds, critically discuss them, and provide challenges for further study. Besides, we introduce modeling standards for roads and discuss the components, types, and distinctions of various LiDAR measurement systems. Then, we review state-of-the-art methods and provide a detailed examination of road segmentation and feature extraction. Furthermore, we systematically introduce point cloud-based 3D modeling methods, namely, parametric modeling and surface reconstruction. Parameters and rules are used to define model components based on geometric and non-geometric information, whereas surface modeling is conducted through individual faces within its geometry. Finally, we discuss and summarize future research directions in this field. This review can assist researchers in enhancing existing approaches and developing new techniques for road modeling based on LiDAR point clouds.