Semi Supervised LiDAR

Semi-supervised learning for LiDAR semantic segmentation aims to reduce the heavy annotation burden of training accurate 3D scene understanding models by leveraging both labeled and unlabeled LiDAR data. Current research focuses on developing novel techniques like consistency learning across multiple LiDAR representations, cross-modal learning with 2D imagery, and active learning strategies guided by LiDAR data to efficiently select samples for annotation. These advancements significantly improve the accuracy and efficiency of LiDAR-based perception systems, impacting applications such as autonomous driving and robotics by enabling the development of more robust and scalable solutions with reduced data labeling costs.