Research on the 3D Fine Modeling Method of In-Service Road

Abstract

To achieve the digitization of all traffic infrastructure elements and enable three-dimensional digital representation of physical facilities, a multilevel road three-dimensional reverse modeling method is proposed based on road point cloud data obtained by a vehicle laser scanning system. First, based on the distribution characteristics of each target structure in the road scene and the modeling requirements, a levels of detail (LOD) modeling specification is designed, and the required feature data format for each level is defined. Next, the three-dimensional characteristic parameters needed for modeling are extracted from the vehicle point cloud data. Finally, the continuous quadrilateral algorithm is used to reconstruct the road model, the topological structure relationship algorithm is used to reconstruct the intersection model, and the instantiation lofting technology is used to reconstruct the rod-shaped target model, all based on the three-dimensional modeling platform. This approach allows for the rapid reverse reconstruction of three-dimensional road models with different LOD levels. The point cloud data of two sections of urban roads with different slopes and one section of expressways were modeled and compared with the original vehicle-mounted laser point cloud data. The data volume of different level models decreases with decreasing model fineness, and the LOD1 level model has the highest similarity with point cloud data, at approximately 92.17%. The similarity of LOD2 and LOD3 decreases in turn, at 82.91% and 75.25%, respectively. For flat or undulating roads, the overall accuracy of the nearest point distance between different levels of road models and point cloud data is better than 10 cm, significantly higher than that of traditional manual modeling. The results demonstrate that vehicle mobile laser scanning technology provides new modeling data for the rapid realization of three-dimensional reverse reconstruction of large-scale traffic infrastructure. Automatic modeling technology can effectively improve modeling efficiency, reduce data redundancy, and ensure model quality.

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“…This approach addresses the challenge of handling unorganized point clouds and complex environments, demonstrating high accuracy in semantic segmentation and geometric precision, which is crucial for practical applications in road engineering [22]. Qi et al (2023) present a method for three-dimensional fine modeling of in-service roads using vehicle laser scanning technology. It focuses on multi-level reverse modeling based on point cloud data, offering a structured approach to reduce data redundancy and enhance model accuracy.…”

“…It focuses on multi-level reverse modeling based on point cloud data, offering a structured approach to reduce data redundancy and enhance model accuracy. The method supports various levels of detail (LOD), allowing for tailored model fineness and application-specific accuracy, which proves effective in managing complex road infrastructures [23]. Wei et al (2023) present an innovative approach for simultaneous multi-curve highway reconstruction from mobile laser scanning data, leveraging deep reinforcement learning with the proximal policy optimization algorithm.…”