3D semantic segmentation under adverse weather conditions

The accurate 3D comprehension of point cloud scenes in diverse weather conditions holds paramount significance in various applications such as autonomous driving in contemporary automobiles, outdoor operations of robots, and autonomous drones. Presently, the majority of studies on semantic segmentation algorithms for 3D point clouds primarily focus on clear weather conditions. However, adverse weather conditions introduce specific types of noise that significantly deteriorate the quality of point clouds. Consequently, this poses a challenge in achieving high accuracy and efficiency in point cloud semantic segmentation for outdoor large-scale scenarios. To tackle this issue, this paper presents a novel semantic segmentation method designed for large scenes encompassing point cloud and foggy weather conditions. We further validate our approach using the Foggy Semantickitti dataset, thereby effectively enhancing the average cross-parallel ratio while maintaining computational efficiency.