Accelerating Training Convergence for Point Cloud Semantic Segmentation of Large-Scale Urban Scenes With Scene-Ensemble Prototypes (opens in new tab)

Point cloud semantic segmentation serves as a vital means of remote sensing, with the existing segmentation networks capable of achieving commendable results. However, the complex network architecture and extensive training data often demand substantial time and computational resources for model convergence. This study proposes a novel method to significantly expedite the convergence of point cloud semantic segmentation networks to save computational resources, which is called rapid convergen...