Road Surface Reconstruction

Road surface reconstruction aims to create accurate 3D models of roads from various data sources like cameras and LiDAR, crucial for autonomous driving and urban planning. Current research emphasizes efficient and scalable methods, employing architectures such as Gaussian splatting (in 2D and 3D variants) and neural networks (including MLPs) to represent road geometry, color, and semantics, often within a bird's-eye-view framework. These advancements address challenges like data sparsity, occlusion, and computational cost, improving the accuracy and speed of reconstruction. The resulting high-fidelity road models enhance autonomous vehicle navigation, mapping, and the development of safer and more efficient transportation systems.