恶劣天气下基于退化特征一致性学习的点云语义分割方法

安怡; 刘书信; 贺丽君; 李凡

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恶劣天气下基于退化特征一致性学习的点云语义分割方法

更新时间：2026-09-02

- 恶劣天气下基于退化特征一致性学习的点云语义分割方法
- Point Cloud Semantic Segmentation Based on Degraded Feature Consistency Learning under Adverse Weather Conditions
- 西安交通大学学报 2026年
- 作者机构：
  
  1.西安交通大学电子与信息学部，710049，西安
  2.西南技术物理研究所，610041，成都
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TP391
- 收稿：2026-02-01，
  
  修回：2026-08-04，
  
  录用：2026-08-05，
  
  网络首发：2026-09-10，
- 稿件说明：
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安怡, 刘书信, 贺丽君, 等. 恶劣天气下基于退化特征一致性学习的点云语义分割方法[J]. 西安交通大学学报,2026.

AN Yi, LIU Shuxin, HE Lijun, et al. Point Cloud Semantic Segmentation Based on Degraded Feature Consistency Learning under Adverse Weather Conditions[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026.
安怡, 刘书信, 贺丽君, 等. 恶劣天气下基于退化特征一致性学习的点云语义分割方法[J]. 西安交通大学学报,2026. DOI：

AN Yi, LIU Shuxin, HE Lijun, et al. Point Cloud Semantic Segmentation Based on Degraded Feature Consistency Learning under Adverse Weather Conditions[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026. DOI：

摘要

针对雨、雪、雾等恶劣天气造成激光雷达点云点丢失、遮挡和几何扰动，导致三维语义分割性能下降，尤其影响行人、自行车等小目标分割精度的问题，提出一种基于退化特征一致性学习的点云语义分割方法。首先，构建由细粒度语义实例增强和空间分区距离感知扰动组成的数据增强模块，通过几何扰动模拟和小目标实例扩充，增强模型对结构变化及小目标特征的学习能力；其次，利用点云体素特征提取网络对点云进行编码，获得体素特征表示；最后，对体素特征施加随机退化，构建“良好—退化双分支”，并通过一致性约束提升网络在特征受损条件下的稳健表征能力。使用公开SemanticSTF数据集对所提方法进行实验验证，结果表明：该方法能够有效改善恶劣天气下的点云语义分割性能；在性能指标方面，平均交并比较基准模型提高10.7%，并在行人、自行车等小目标类别上取得更优的分割结果。研究结果可为复杂天气环境下三维感知系统的安全可靠应用提供参考。

Abstract

To address the issues of point loss

occlusion

and geometric distortion in LiDAR point clouds caused by adverse weather conditions such as rain

snow

and fog

which lead to degraded performance in 3D semantic segmentation and particularly affect the segmentation accuracy of small objects like pedestrians and bicycles

a point cloud semantic segmentation method is proposed based on degraded feature consistency learning. First

a data augmentation module consisting of fine-grained semantic instance enhancement and spatially partitioned distance-aware perturbation is constructed. Through geometric perturbation simulation and small object instance expansion

the model's learning capability for structural variations and small object features is enhanced. Second

a point cloud voxel feature extraction network is used to encode point clouds to obtain voxel feature representations. Finally

random degradation is applied to voxel features to construct a "clean-degraded dual-branch" structure

and the robust representation capability of the network under feature corruption conditions is improved through consistency constraints. The proposed method is experimentally validated on the public SemanticSTF dataset. The results show that this method can effectively improve the performance of point cloud semantic segmentation under adverse weather conditions. In terms of performance metrics

the mean Intersection over Union (mIoU) is 10.7% higher than that of the baseline model

and better segmentation results are achieved for small object categories such as pedestrians and bicycles. The research results can provide a reference for the safe and reliable application of 3D perception systems in complex weather environments.

关键词

Keywords

references

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