  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/.
