PDLNet: Learning Point Cloud Distortion for Unsupervised Cross-Domain Point Cloud Segmentation in Adverse Weather

Abstract

Existing point cloud semantic segmentation models are usually trained and evaluated using data collected under clear weather conditions. Under adverse weather conditions such as rain, snow and fog, point clouds are usually distorted and significant degradation of existing model performance occurs. Many domain adaptation methods try to address this issue by simulating adverse weather or using data augmentation techniques during training. However, they cannot accurately model the actual distortion in the target domain. By analyzing the visualization and statistical information of the target domain data and referring to existing studies, we categorize the distortion of point cloud data into position distortion, intensity distortion, and quantity distortion. To address these distortions of the target domain data, we propose a Point Distortion Learning Network (PDLNet) to integrate the Point Distortion Learning (PDL) module to learn the feature distortion of target domain data due to the adverse weather. Moreover, we also integrate the Cross-domain Feature Association (CFA) module to assist the model learn domain-invariant feature representations to improve the model's adaptability to the target domain. In addition, PDLNet introduces the Point Semantic Knowledge Distillation (PSKD) module, which ensures that only the target domain data is used efficiently in the inference phase while preserving the learned cross-domain knowledge. To further improve the model performance, we also iteratively optimize the model by introducing the curriculum learning module. Our approach establishes a new state-of-the-art level by achieving 40.6% mIoU and 27.7% mIoU in the SemanticKITTI-to-SemanticSTF and SynLiDAR-to-SemanticSTF benchmarks, respectively. Source code will be released at https://github.com/JerryD233/PDLNet.