Realistic road path reconstruction from GIS data

Nguyen, Hiep H.; Desbenoit, Brett; Daniel, Marc

doi:10.1111/cgf.12494

Cited by 12 publications

(4 citation statements)

References 22 publications

(26 reference statements)

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“…Tree-dimensional laser scanning technology has been widely used in high-precision digital map construction, semantic segmentation of road objects in the whole scene, and other felds [4][5][6] because of its advantages of high precision, fast speed, noncontact, and rapid acquisition of accurate three-dimensional space information of the measured object. Currently, the method of using laser scanning point cloud for road 3D measurement is also relatively mature.…”

Section: Introductionmentioning

confidence: 99%

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

Qi,

Cong,

Liu

et al. 2023

Advances in Civil Engineering

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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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Section: Introductionmentioning

confidence: 99%

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

Qi,

Cong,

Liu

et al. 2023

Advances in Civil Engineering

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“…Numerous methodologies for the rapid 3D reconstruction of roads have been put forth by scholars both domestically and internationally. In the literature, one prominent approach [1] involves generating a 3D road network model based on 2D GIS road centerline data. However, this method necessitates high data accuracy and exhibits limited adaptability when confronted with undulating or varying width roads.…”

Section: Introductionmentioning

confidence: 99%

A 3D modelling method for road based on vehicle laser point cloud

Hu,

Liu,

Xie

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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To address the challenges pertaining to the sluggish 3D reconstruction of the road surface, excessive workload, and limited adaptability, this study undertook the feature extraction of road point cloud data exhibiting diverse linear shapes, leveraging vehicle-mounted laser point cloud data. Additionally, a novel approach was devised to automatically construct the road triangulation network, thereby achieving a high-precision digital restoration of the road surface. Initially, the data attributes of the laser point cloud obtained from road vehicles are considered, and a data preprocessing technique is devised using the Alpha Shapes algorithm and the point cloud grid thinning algorithm. This method aims to retain crucial information pertaining to the elevation of the road surface, linear boundaries of the road surface, and skeletal features of the road surface. Subsequently, a technique is devised for constructing a Delaunay triangulation network using ordered point cloud data, based on the boundary points and feature points of the road surface. Additionally, an investigation is conducted on a method for constructing a road surface triangulation network with road boundary constraints, enabling the identification and removal of triangles located outside the road surface, thereby achieving precise restoration of the road surface. Ultimately, the assessment of the generated 3D pavement model's quality validates the method's feasibility and accuracy.

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“…Tsai, Wu, and Wang (2010) used photographic images to compute roadway curvature. Z. Li, Chitturi, Bill, and Noyce (2012) and Nguyen, Desbenoit, and Daniel (2014) extracted curve information from geographic information system (GIS) maps. Castro, Iglesias, Rodríguez‐Solano, and Sánchez (2006) used global positioning system data to model highways.…”

Section: Introductionmentioning

confidence: 99%

Integrating segmentation and parameter estimation for recreating vertical alignments

Song

Yang

Schonfeld

et al. 2020

Computer aided Civil Eng

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A major problem of vertical alignment recreation is to automatically attribute the measured points to geometric elements (i.e., grades and vertical curves) and to efficiently recreate the vertical alignment with constraints. Most existing methods are nonoptimal in theory, semiautomatic, or inefficient in recreating an alignment. A new approach is proposed for automatically determining segmentation into geometric elements from measured points and efficiently optimizing a recreated alignment with constraints. First, independent parameters defining an alignment, are proposed to represent a vertical alignment. Then, a statistical deflection angle (SDA) method is proposed to determine segmentation by exploring statistical features of the geometric elements. Analysis shows that the SDA method outperforms the curvature method in distinguishing between grades and curves. Patterns of the segmentation process are found, and a segmentation algorithm is provided. Further, an optimization model is proposed to recreate the alignment with constraints. Experiment results demonstrate that this approach is highly efficient and effective compared with existing methods, reducing the number of searched alignments from tens of thousands to tens, while improving the value of the objective function.

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Realistic road path reconstruction from GIS data

Cited by 12 publications

References 22 publications

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

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

A 3D modelling method for road based on vehicle laser point cloud

Integrating segmentation and parameter estimation for recreating vertical alignments

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