NEWS ARTICLE
学术动态：LC-HR2FNet: High-Resolution Early-Level Fusion-Based LiDAR-Camera Network for Accurate Road Segmentation Autonomou.
论文标题：LC-HR2FNet: High-Resolution Early-Level Fusion-Based LiDAR-Camera Network for Accurate Road Segmentation Autonomous Driving
发布日期：2026-05-22
作者：Lele Wang, Ming Li, Peng Zhang
DOI：10.3390/s26113281
论文摘要：Accurate road segmentation is a core perceptual technology for autonomous driving, but faces two challenges: (1) ambiguous road boundaries caused by insufficient modeling of contextual information relationships in CNN-based networks and (2) inadequate LiDAR-camera fusion due to modality gaps between heterogeneous sensors. To mitigate these limitations, this paper proposes a novel approach, named LiDAR-Camera High-Resolution Feature Fusion Network (LC-HR2FNet), a multi-cross-stage fusion model designed for road segmentation. Firstly, a new type of pseudo-LiDAR-Image representation is generated via an early-level fusion strategy and data complementation. Sparse point clouds are transformed into dense LiDAR-Image data and then concatenated with RGB channel maps to form complementary multi-modal data inputs. Subsequently, a modified HRNet backbone integrated with cross-stage feature fusion is constructed to strengthen information interaction across different branches and enhance the modeling of contextual relationships. Additionally, a dilated feature collection model is designed to collect multi-scale confidence scores for pixel-wise class determination. Experiments on the KITTI road benchmark demonstrate that the proposed method achieves a MaxF of 97.39% on UMM_ROAD and an average of 96.28% across all urban scenarios, demonstrating superior performance and robustness.
元数据：Crossref 收录的 MDPI Sensors 论文。 DOI: 10.3390/s26113281. Vol. 26, Issue 11. Authors: Lele Wang, Ming Li, Peng Zhang.
开放许可：https://creativecommons.org/licenses/by/4.0/
原文链接：https://doi.org/10.3390/s26113281
PDF 链接：https://www.mdpi.com/1424-8220/26/11/3281/pdf