Curb detection is an important function in intelligent driving and can be
used to determine drivable areas of the road. However, curbs are difficult to
detect due to the complex road environment. This paper introduces CurbNet, a
novel framework for curb detection, leveraging point cloud segmentation.
Addressing the dearth of comprehensive curb datasets and the absence of 3D
annotations, we have developed the 3D-Curb dataset, encompassing 7,100 frames,
which represents the largest and most categorically diverse collection of curb
point clouds currently available. Recognizing that curbs are primarily
characterized by height variations, our approach harnesses spatially-rich 3D
point clouds for training. To tackle the challenges presented by the uneven
distribution of curb features on the xy-plane and their reliance on z-axis
high-frequency features, we introduce the multi-scale and channel attention
(MSCA) module, a bespoke solution designed to optimize detection performance.
Moreover, we propose an adaptive weighted loss function group, specifically
formulated to counteract the imbalance in the distribution of curb point clouds
relative to other categories. Our extensive experimentation on 2 major datasets
has yielded results that surpass existing benchmarks set by leading curb
detection and point cloud segmentation models. By integrating multi-clustering
and curve fitting techniques in our post-processing stage, we have
substantially reduced noise in curb detection, thereby enhancing precision to
0.8744. Notably, CurbNet has achieved an exceptional average metrics of over
0.95 at a tolerance of just 0.15m, thereby establishing a new benchmark.
Furthermore, corroborative real-world experiments and dataset analyzes mutually
validate each other, solidifying CurbNet's superior detection proficiency and
its robust generalizability.

CurbNet 是一种使用点云分割进行路边检测的新型框架，通过利用高度变化来训练模型，采用多尺度和通道注意力模块以及曲线拟合技术，在噪音减少、检测精度提高的同时，实现了卓越的检测性能和鲁棒的泛化能力，创立了新的基准水平。

基于 LiDAR 点云分割的路缘检测框架 CurbNet

CurbNet: Curb Detection Framework Based on LiDAR Point Cloud  Segmentation

Curb detection is essential for environmental awareness in Automated Driving
(AD), as it typically limits drivable and non-drivable areas. Annotated data
are necessary for developing and validating an AD function. However, the number
of public datasets with annotated point cloud curbs is scarce. This paper
presents a method for detecting 3D curbs in a sequence of point clouds captured
from a LiDAR sensor, which consists of two main steps. First, our approach
detects the curbs at each scan using a segmentation deep neural network. Then,
a sequence-level processing step estimates the 3D curbs in the reconstructed
point cloud using the odometry of the vehicle. From these 3D points of the
curb, we obtain polylines structured following ASAM OpenLABEL standard. These
detections can be used as pre-annotations in labelling pipelines to efficiently
generate curb-related ground truth data. We validate our approach through an
experiment in which different human annotators were required to annotate curbs
in a group of LiDAR-based sequences with and without our automatically
generated pre-annotations. The results show that the manual annotation time is
reduced by 50.99% thanks to our detections, keeping the data quality level.

本论文介绍了一种从 LiDAR 传感器捕获的一系列点云中检测三维路缘的方法，该方法包括两个主要步骤：利用分割深度神经网络在每次扫描中检测路缘；然后，利用车辆的里程计数据在重建的点云中估计三维路缘。通过实验证实了该方法的有效性并验证了其可以显著减少手动注释时间并保持数据质量水平。