In computer vision and graphics, the accurate reconstruction of road surfaces
is pivotal for various applications, especially in autonomous driving. This
paper introduces a novel method leveraging the Multi-Layer Perceptrons (MLPs)
framework to reconstruct road surfaces in height, color, and semantic
information by input world coordinates x and y. Our approach NeRO uses encoding
techniques based on MLPs, significantly improving the performance of the
complex details, speeding up the training speed, and reducing neural network
size. The effectiveness of this method is demonstrated through its superior
performance, which indicates a promising direction for rendering road surfaces
with semantics applications, particularly in applications demanding
visualization of road conditions, 4D labeling, and semantic groupings.

本研究介绍了一种利用多层感知机（MLPs）框架来通过输入的世界坐标 x 和 y 在高度、颜色和语义信息方面重构道路表面的新方法。我们的方法 NeRO 使用基于 MLPs 的编码技术，显著提高了对复杂细节的性能，并加快了训练速度，减小了神经网络的大小。通过其卓越的性能，我们证明了该方法的有效性，这表明了在语义应用中渲染道路表面的有希望的方向，特别适用于需要可视化道路状况、4D 标签和语义分组的应用。

NeRO：神经道路表面重建

NeRO: Neural Road Surface Reconstruction

This paper addresses the growing demands for safety and comfort in
intelligent robot systems, particularly autonomous vehicles, where road
conditions play a pivotal role in overall driving performance. For example,
reconstructing road surfaces helps to enhance the analysis and prediction of
vehicle responses for motion planning and control systems. We introduce the
Road Surface Reconstruction Dataset (RSRD), a real-world, high-resolution, and
high-precision dataset collected with a specialized platform in diverse driving
conditions. It covers common road types containing approximately 16,000 pairs
of stereo images, original point clouds, and ground-truth depth/disparity maps,
with accurate post-processing pipelines to ensure its quality. Based on RSRD,
we further build a comprehensive benchmark for recovering road profiles through
depth estimation and stereo matching. Preliminary evaluations with various
state-of-the-art methods reveal the effectiveness of our dataset and the
challenge of the task, underscoring substantial opportunities of RSRD as a
valuable resource for advancing techniques, e.g., multi-view stereo towards
safe autonomous driving. The dataset and demo videos are available at
this https URL

通过对道路表面的重建，提高自主车辆的分析和预测能力，本研究介绍了道路表面重建数据集（RSRD），并构建了一个综合性基准用于恢复道路剖面的深度估计和立体匹配任务。初步评估结果显示我们的数据集的有效性，并强调了 RSRD 作为推动安全自动驾驶等技术的有价值资源。

RSRD：安全舒适自动驾驶的道路表面重建数据集和基准

RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and  Comfortable Autonomous Driving

Research on damage detection of road surfaces using image processing
techniques has been actively conducted, achieving considerably high detection
accuracies. Many studies only focus on the detection of the presence or absence
of damage. However, in a real-world scenario, when the road managers from a
governing body need to repair such damage, they need to clearly understand the
type of damage in order to take effective action. In addition, in many of these
previous studies, the researchers acquire their own data using different
methods. Hence, there is no uniform road damage dataset available openly,
leading to the absence of a benchmark for road damage detection. This study
makes three contributions to address these issues. First, to the best of our
knowledge, for the first time, a large-scale road damage dataset is prepared.
This dataset is composed of 9,053 road damage images captured with a smartphone
installed on a car, with 15,435 instances of road surface damage included in
these road images. In order to generate this dataset, we cooperated with 7
municipalities in Japan and acquired road images for more than 40 hours. These
images were captured in a wide variety of weather and illuminance conditions.
In each image, we annotated the bounding box representing the location and type
of damage. Next, we used a state-of-the-art object detection method using
convolutional neural networks to train the damage detection model with our
dataset, and compared the accuracy and runtime speed on both, using a GPU
server and a smartphone. Finally, we demonstrate that the type of damage can be
classified into eight types with high accuracy by applying the proposed object
detection method. The road damage dataset, our experimental results, and the
developed smartphone application used in this study are publicly available
(this https URL).

本研究通过使用卷积神经网络进行物体检测的最新方法，创建了一个可供公开使用的大型路面损伤数据集，该数据集包含了来自 7 个日本市的 9053 张路面损伤图片，针对这个数据集训练出有较高准确性的路面损伤检测模型，并成功将损伤分为八个类型。