Curb detection is a crucial function in intelligent driving, essential for determining drivable areas on the road. However, the complexity of road environments makes curb detection challenging. This paper introduces CurbNet, a novel framework for curb detection utilizing point cloud segmentation. To address the lack of comprehensive curb datasets with 3D annotations, we have developed the 3D-Curb dataset based on SemanticKITTI, currently the largest and most diverse collection of curb point clouds. Recognizing that the primary characteristic of curbs is height variation, our approach leverages spatially rich 3D point clouds for training. To tackle the challenges posed by the uneven distribution of curb features on the xy-plane and their dependence on high-frequency features along the z-axis, we introduce the Multi-Scale and Channel Attention (MSCA) module, a customized solution designed to optimize detection performance. Additionally, 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. Extensive experiments conducted on 2 major datasets demonstrate that our method surpasses existing benchmarks set by leading curb detection and point cloud segmentation models. Through the post-processing refinement of the detection results, we have significantly reduced noise in curb detection, thereby improving precision by 4.5 points. Similarly, our tolerance experiments also achieved state-of-the-art results. Furthermore, real-world experiments and dataset analyses mutually validate each other, reinforcing CurbNet's superior detection capability and robust generalizability. The project website is available at: https://github.com/guoyangzhao/CurbNet/.

翻译：路缘石检测是智能驾驶中的关键功能，对于确定道路可行驶区域至关重要。然而，道路环境的复杂性使得路缘石检测具有挑战性。本文提出了CurbNet，一种利用点云分割进行路缘石检测的新型框架。针对缺乏具有3D标注的综合性路缘石数据集的问题，我们基于SemanticKITTI开发了3D-Curb数据集，这是目前规模最大、多样性最丰富的路缘石点云集合。认识到路缘石的主要特征是高度变化，我们的方法利用空间信息丰富的3D点云进行训练。为了解决路缘石特征在xy平面上分布不均且依赖于z轴高频特征所带来的挑战，我们引入了多尺度与通道注意力（MSCA）模块，这是一种为优化检测性能而定制的解决方案。此外，我们提出了一种自适应加权损失函数组，专门用于应对路缘石点云相对于其他类别分布不均衡的问题。在两个主要数据集上进行的大量实验表明，我们的方法超越了现有领先的路缘石检测和点云分割模型所设定的基准。通过对检测结果进行后处理优化，我们显著降低了路缘石检测中的噪声，从而将精确度提高了4.5个百分点。同样，我们的容错实验也取得了最先进的结果。此外，实际道路实验与数据集分析相互验证，共同证实了CurbNet卓越的检测能力和强大的泛化性能。项目网站地址为：https://github.com/guoyangzhao/CurbNet/。