ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Computer Vision
Volume 7 - 2025 | doi: 10.3389/fcomp.2025.1542813
RWAFormer:A Lightweight Road LiDAR Point Cloud Segmentation Network based on Transformer
Provisionally accepted
- Changchun University of Science and Technology, Changchun, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Point cloud semantic segmentation technology for road scenes plays an important role in the field of autonomous driving. However, accurate semantic segmentation of large-scale and non-uniformly dense LiDAR road point clouds still faces severe challenges. To this end, this paper proposes a road point cloud semantic segmentation algorithm called RWAFormer. First, a sparse tensor feature encoding module (STFE) is introduced to enhance the network's ability to extract local features of point clouds. Secondly, a radial window attention module (RWA) is designed to dynamically select the neighborhood window size according to the distance of the point cloud data from the center point, effectively aggregating the information of long-distance sparse point clouds to the adjacent dense areas, significantly improving the segmentation effect of long-distance point clouds. Experimental results show that our method achieves an average intersection over union (mIoU) of 75.3% and 82.0% on the Semantickitti and Nuscenes datasets, and an accuracy (Acc) of 94.5% and 97.4%. These results validate the effectiveness and superiority of RWAFormer in road point cloud semantic segmentation.
Keywords: point cloud semantic segmentation, Road scene, lidar, Autonomousdriving, transformer
Received: 10 Dec 2024; Accepted: 22 Sep 2025.
Copyright: © 2025 Li, Chen, Liu, Zhao and Guan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lei Chen, leichen@cust.edu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.