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.
@article{arxiv.2405.10554,
title = {NeRO: Neural Road Surface Reconstruction},
author = {Ruibo Wang and Song Zhang and Ping Huang and Donghai Zhang and Haoyu Chen},
journal= {arXiv preprint arXiv:2405.10554},
year = {2024}
}