Name
Abstract | ||
|---|---|---|
How to automatically generate a realistic large-scale 3D road network is a key point for immersive and credible traffic simulations. Existing methods cannot automatically generate various kinds of intersections in 3D space based on GIS data. In this paper, we propose a method to generate complex and large-scale 3D road networks automatically with the open source GIS data, including satellite imagery, elevation data and two-dimensional(2D) road center axis data, as input. We first introduce a semantic structure of road network to obtain high-detailed and well-formed networks in a 3D scene. We then generate 2D shapes and topological data of the road network according to the semantic structure and 2D road center axis data. At last, we segment the elevation data and generate the surface of the 3D road network according to the 2D semantic data and satellite imagery data. Results show that our method does well in the generation of various types of intersections and the high-detailed features of roads. The traffic semantic structure, which must be provided in traffic simulation, can also be generated automatically according to our method.(c) 2021 Elsevier Ltd. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.cag.2021.02.004 | COMPUTERS & GRAPHICS-UK |
Keywords | DocType | Volume |
GIS Data, Large-scale 3D road scenes, Automatic generation, Traffic simulation | Journal | 96 |
ISSN | Citations | PageRank |
0097-8493 | 1 | 0.36 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Wang | 1 | 130 | 24.62 |
| Yue Wu | 2 | 331 | 31.69 |
| Xu Han | 3 | 71 | 10.74 |
| Mingliang Xu | 4 | 140 | 12.95 |
| Weizhe Chen | 5 | 1 | 0.36 |
| Guoliang Chen | 6 | 305 | 46.48 |