PDAST: Paradigm Towards Weather Domain Adaptation for 3D Detection Based on Density Aware Pooling and Self-Training

Most 3D detection algorithms are developed and validated on datasets collected during clear weather, while performance significantly deteriorates under severe weather conditions such as snowy days. To address this problem, we propose PDAST, an unsupervised pipeline for weather domain adaptation. PDAST involves point density dealing (PDD), a data augmentation method, and a density-aware pooling module (PDV), both of which are plug-and-play modules to enhance algorithmic robustness in snowy weather conditions along with the self-training [1] strategy. The data augmentation method PDD randomly samples and perturbs the ground truth point database in the point cloud. Meanwhile, the PDV module combines voxel features and density-based positional encoding features as inputs to a multi-head self-attention module, facilitating the capture of global features comprising density features. In this paper, we validate the effectiveness of PDAST on a typical voxel-based 3D detection baseline, SecondIOU [2], and conduct experiments on STF [3] datasets with clear and snowy datasets. Our approach PDAST achieves a performance enhancement of around 14.9% ~ 25.5% for $AP_{3D}$ (Average Precision Of 3D Boundingboxes) and 17.5% ~ 29.3% for $AP_{AOS}$ (Average Precision Of Orientation Similarity) compared to the baseline detector on clear-to-snowy conditions. Furthermore, PDAST's performance in unsupervised clear-to-snowy conditions matches or exceeds the baseline model fully supervised on snowy conditions. Our PDAST alleviates the performance degradation caused by severe weather, such as snowy days, and is of great significance for all-weather perception.