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Open Access

ARTICLE

Mamba-Transformer: A LiDAR Semantic Segmentation Algorithm for Adverse Weather

Lili Zhang, Jinming Cheng*, Yingyou Wen
School of Computer Science and Engineering, Northeastern University, Shenyang, China
* Corresponding Author: Jinming Cheng. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.086285

Received 27 May 2026; Accepted 20 August 2026; Published online 17 September 2026

Abstract

Adverse weather conditions substantially degrade the performance of light detection and ranging (LiDAR) semantic segmentation because weather-induced interference often intensifies point-cloud noise, weakens the representation of long-range objects, and causes the loss of local structural details. These degradations make it difficult for segmentation models to maintain reliable perception across regions with different levels of uncertainty and complexity. To address these challenges, this paper proposes a reliability-guided Mamba–Transformer hybrid framework for adverse-weather LiDAR semantic segmentation. The proposed method first evaluates the reliability and difficulty of adverse-weather point clouds and constructs region-level priors to guide subsequent feature modeling and semantic inference. Based on these priors, the Mamba architecture is employed to capture global contextual dependencies, enabling effective modeling of long-range semantic relationships in degraded point clouds. Meanwhile, a Transformer module is introduced to perform local refinement on critical and difficult regions, thereby enhancing the discrimination of complex structures and improving segmentation robustness. Finally, an adaptive gating mechanism fuses global and local multi-source features so that complementary information from different modeling branches can be integrated for final semantic prediction. Experimental results on the SemanticSTF dataset demonstrate that the proposed method achieves strong overall performance in adverse-weather LiDAR semantic segmentation and shows advantages in overall segmentation accuracy and the recognition of complex regions.

Keywords

LiDAR semantic segmentation; adverse weather; reliability guidance; Mamba; Transformer; RMT-Seg

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