  Accurately reconstructing road surfaces is pivotal for various applications
especially in autonomous driving. This paper introduces a position encoding
Multi-Layer Perceptrons (MLPs) framework to reconstruct road surfaces, with
input as world coordinates x and y, and output as height, color, and semantic
information. The effectiveness of this method is demonstrated through its
compatibility with a variety of road height sources like vehicle camera poses,
LiDAR point clouds, and SFM point clouds, robust to the semantic noise of
images like sparse labels and noise semantic prediction, and fast training
speed, which indicates a promising application for rendering road surfaces with
semantics, particularly in applications demanding visualization of road
surface, 4D labeling, and semantic groupings.
