FlexRoad

FlexRoad is a pioneering framework designed to enhance road surface reconstruction, transforming noisy 3D point cloud data from sources like photogrammetry or geodata providers into smooth, accurate, and realistic road meshes. This process is critical for applications such as autonomous driving, urban planning, and virtual world simulations, where the quality of road surface representation significantly impacts performance and realism.

The core of FlexRoad's methodology involves fitting Non-Uniform Rational B-Splines (NURBS) surfaces to the 3D road points. A key innovation is the Elevation-Constrained Spatial Road Clustering (ECSRC) algorithm, which robustly corrects anomalies and substantially reduces surface roughness and fitting errors. The project also introduces the GeoRoad Dataset (GeRoD), a diverse collection of road surface and terrain profiles, alongside experiments on the DeepScenario Open 3D Dataset (DSC3D). These datasets facilitate quantitative comparisons, demonstrating FlexRoad's superior performance over conventional road surface representations across various metrics and its adaptability to diverse input sources, terrains, and noise types. Its comprehensive approach makes FlexRoad a generic solution for realistic road surface modeling.