Enhancing LiDAR Semantic Segmentation Using Model Soups

Omar Wasfy, Sohier Basiony, Marwan Torki

Abstract: Semantic segmentation of Light Detection and Ranging (LiDAR) is a task that requires high efficiency and accuracy. To our knowledge, this work is the first to apply model soups to the LiDAR semantic segmentation task, showcasing their potential impact on the domain. Our contributions in this work are twofold: First, we successfully extend the application of model soups to LiDAR semantic segmentation. Second, we introduce an optimized and efficient version of the existing greedy soup, further enhancing the overall performance of the approach. In our method, We augment the state-of-the-art open-source code for 2DPASS semantic segmentation with our technique, retaining the original model structure and ensuring no increase in prediction time. The efficiency of our approach is demonstrated using Mean Intersection Over Union (MIoU) as the primary evaluation metric. Our experiments on the SemanticKITTI and NuScenes datasets demonstrate significant improvements. Specifically, we achieve a higher MIoU without any increase in prediction time. Our results, through comprehensive experiments and rigorous evaluations, open up new possibilities for enhanced perception systems in autonomous driving and related fields, highlighting the significance of our iterative uniform greedy model soup in advancing LiDAR semantic segmentation.